Signal processing apparatus

ABSTRACT

A signal processor which acquires a first signal, including a first primary signal portion and a first secondary signal portion, and a second signal, including a second primary signal portion and a second secondary signal portion, wherein the first and second primary signal portions are correlated. The signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signals may be acquired by measuring energy generated by the medium. A processor of the present invention generates a primary or secondary reference signal which is a combination, respectively, of only the primary or secondary signal portions. The secondary reference signal is then used to remove the secondary portion of each of the first and second measured signals via a correlation canceler, such as an adaptive noise canceler, preferably of the joint process estimator type. The primary reference signal is used to remove the primary portion of each of the first and second measured signals via a correlation canceler. The processor of the present invention may be employed in conjunction with a correlation canceler in physiological monitors wherein the known properties of energy attenuation through a medium are used to determine physiological characteristics of the medium. Many physiological conditions, such as the pulse, or blood pressure of a patient or the concentration of a constituent in a medium, can be determined from the primary or secondary portions of the signal after other signal portion is removed.

FIELD OF THE INVENTION

[0001] The present invention relates to the field of signal processing.More specifically, the present invention relates to the processing ofmeasured signals, containing a primary and a secondary signal, for theremoval or derivation of either the primary or secondary signal whenlittle is known about either of these components. The present inventionalso relates to the use of a novel processor which in conjunction with acorrelation canceler, such as an adaptive noise canceler, producesprimary and/or secondary signals. The present invention is especiallyuseful for physiological monitoring systems including blood oxygensaturation.

BACKGROUND OF THE INVENTION

[0002] Signal processors are typically employed to remove or deriveeither the primary or secondary signal portion from a composite measuredsignal including a primary signal portion and a secondary signalportion. If the secondary signal portion occupies a different frequencyspectrum than the primary signal portion, then conventional filteringtechniques such as low pass, band pass, and high pass filtering could beused to remove or derive either the primary or the secondary signalportion from the total signal. Fixed single or multiple notch filterscould also be employed if the primary and/or secondary signal portion(s)exit at a fixed frequency(s).

[0003] It is often the case that an overlap in frequency spectrumbetween the primary and secondary signal portions exists. Complicatingmatters further, the statistical properties of one or both of theprimary and secondary signal portions change with time. In such cases,conventional filtering techniques are totally ineffective in extractingeither the primary or secondary signal. If, however, a description ofeither the primary or secondary signal portion can be made availablecorrelation canceling, such as adaptive noise canceling, can be employedto remove either the primary or secondary signal portion of the signalleaving the other portion available for measurement.

[0004] Correlation cancelers, such as adaptive noise cancelers,dynamically change their transfer function to adapt to and remove eitherthe primary or secondary signal portions of a composite signal.Correlation cancelers require either a secondary reference or a primaryreference which is correlated to either the secondary signal or theprimary signal portions only. The reference signals are not necessarilya representation of the primary or secondary signal portions, but have afrequency spectrum which is similar to that of the primary or secondarysignal portions. In many cases, it requires considerable ingenuity todetermine a reference signal since nothing is usually known a prioriabout the secondary and/or primary signal portions.

[0005] One area where composite measured signals comprising a primarysignal portion and a secondary signal portion about which no informationcan easily be determined is physiological monitoring. Physiologicalmonitoring apparatuses generally measure signals derived from aphysiological system, such as the human body. Measurements which aretypically taken with physiological monitoring systems includeelectrocardiographs, blood pressure, blood gas saturation (such asoxygen saturation), capnographs, heart rate, respiration rate, and depthof anesthesia, for example. Other types of measurements include thosewhich measure the pressure and quantity of a substance within the bodysuch as breathalyzer testing, drug testing, cholesterol testing, glucosetesting, arterial carbon dioxide testing, protein testing, and carbonmonoxide testing, for example. Complications arising in thesemeasurements are often due to motion of the patient, both external andinternal (muscle movement, for example), during the measurement process.

[0006] Knowledge of physiological systems, such as the amount of oxygenin a patient's blood, can be critical, for example during surgery. Thesedata can be determined by a lengthy invasive procedure of extracting andtesting matter, such as blood, from a patient, or by more expedient,non-invasive measures. Many types of non-invasive measurements can bemade by using the known properties of energy attenuation as a selectedform of energy passes through a medium.

[0007] Energy is caused to be incident on a medium either derived fromor contained within a patient and the amplitude of transmitted orreflected energy is then measured. The amount of attenuation of theincident energy caused by the medium is strongly dependent on thethickness and composition of the medium through which the energy mustpass as well as the specific form of energy selected. Information abouta physiological system can be derived from data taken from theattenuated signal of the incident energy transmitted through the mediumif either the primary or secondary signal of the composite measurementsignal can be removed. However, non-invasive measurements often do notafford the opportunity to selectively observe the interference causingeither the primary or secondary signal portions, making it difficult toextract either one of them from the composite signal.

[0008] The primary and/or secondary signal portions often originate fromboth AC and/or DC sources. The DC portions are caused by transmission ofthe energy through differing media which are of relatively constantthickness within the body, such as bone, tissue, skin, blood, etc. Theseportions are easy to remove from a composite signal. The AC componentsare caused by physiological pulsations or when differing media beingmeasured are perturbed and thus, change in thickness while themeasurement is being made. Since most materials in and derived from thebody are easily compressed, the thickness of such matter changes if thepatient moves during a non-invasive physiological measurement. Patientmovement, muscular movement and vessel movement, can cause theproperties of energy attenuation to vary erratically. Traditional signalfiltering techniques are frequently totally ineffective and grosslydeficient in removing these motion induced effects from a signal. Theerratic or unpredictable nature of motion induced signal components isthe major obstacle in removing or deriving them. Thus, presentlyavailable physiological monitors generally become totally inoperativeduring time periods when the measurement site is perturbed.

[0009] A blood gas monitor is one example of a physiological monitoringsystem which is based upon the measurement of energy attenuated bybiological tissues or substances. Blood gas monitors transmit light intothe tissue and measure the attenuation of the light as a function oftime. The output signal of a blood gas monitor which is sensitive to thearterial blood flow contains a component which is a waveformrepresentative of the patient's arterial pulse. This type of signal,which contains a component related to the patient's pulse, is called aplethysmographic wave, and is shown in FIG. 1 as curve s.Plethysmographic waveforms are used in blood pressure or blood gassaturation measurements, for example. As the heart beats, the amount ofblood in the arteries increases and decreases, causing increases anddecreases in energy attenuation, illustrated by the cyclic wave s inFIG. 1.

[0010] Typically, a digit such as a finger, an ear lobe, or otherportion of the body where blood flows close to the skin, is employed asthe medium through which light energy is transmitted for blood gasattenuation measurements. The finger comprises skin, fat, bone, muscle,etc., shown schematically in FIG. 2, each of which attenuates energyincident on the finger in a generally predictable and constant manner.However, when fleshy portions of the finger are compressed erratically,for example by motion of the finger, energy attenuation becomes erratic.

[0011] An example of a more realistic measured waveform S is shown inFIG. 3, illustrating the effect of motion. The primary plethysmographicwaveform portion of the signal s is the waveform representative of thepulse, corresponding to the sawtooth-like pattern wave in FIG. 1. Thelarge, secondary motion-induced excursions in signal amplitude hide theprimary plethysmographic signal s. It is easy to see how even smallvariations in amplitude make it difficult to distinguish the primarysignal s in the presence of a secondary signal component n.

[0012] A specific example of a blood gas monitoring apparatus is a pulseoximeter which measures the arterial saturation of oxygen in the blood.The pumping of the heart forces freshly oxygenated blood into thearteries causing greater energy attenuation. The arterial saturation ofoxygenated blood may be determined from the depth of the valleysrelative to the peaks of two plethysmographic waveforms measured atseparate wavelengths. Patient movement introduces signal portions mostlydue to venous blood, or motion artifacts, to the plethysmographicwaveform illustrated in FIG. 3. It is these motion artifacts which mustbe removed from the measured signal for the oximeter to continue themeasurement of arterial blood oxygen saturation, even during periodswhen the patient moves. It is also these motion artifacts which must bederived from the measured signal for the oximeter to obtain an estimateof venous blood oxygen saturation. Once the signal components due toeither arterial blood or venous blood is known, its corresponding oxygensaturation may be determined.

SUMMARY OF THE INVENTION

[0013] This invention is an improvement of U.S. patent application Ser.No. 07/666,060 filed Mar. 7, 1991 and entitled Signal ProcessingApparatus and Method which earlier application has been assigned to theassignee of the instant application. The invention is a signal processorwhich acquires a first signal and a second signal that is correlated tothe first signal. The first signal comprises a first primary signalportion and a first secondary signal portion. The second signalcomprises a second primary signal portion and a second secondary signalportion. The signals may be acquired by propagating energy through amedium and measuring an attenuated signal after transmission orreflection. Alternatively, the signals may be acquired by measuringenergy generated by the medium.

[0014] The first and second measured signals are processed to generate asecondary reference which does not contain the primary signal portionsfrom either of the first or second measured signals. The remainingsecondary signal portions from the first and second measured signals arecombined to form the secondary reference. This secondary reference iscorrelated to the secondary signal portion of each of the first andsecond measured signals.

[0015] The secondary reference is then used to remove the secondaryportion of each of the first and second measured signals via acorrelation canceler, such as an adaptive noise canceler. Thecorrelation canceler is a device which takes a first and second inputand removes from the first input all signal components which arecorrelated to the: second input. Any unit which performs or nearlyperforms this function is herein considered to be a correlationcanceler. An adaptive correlation canceler can be described by analogyto a dynamic multiple notch filter which dynamically changes itstransfer function in response to a reference signal and the measuredsignals to remove frequencies from the measured signals that are alsopresent in the reference signal. Thus, a typical adaptive correlationcanceler receives the signal from which it is desired to remove acomponent and a reference signal. The output of the correlation canceleris a good approximation to the desired signal with the undesiredcomponent removed.

[0016] Alternatively, the first and second measured signals may beprocessed to generate a primary reference which does not contain thesecondary signal portions from either of the first or second measuredsignals. The remaining primary signal portions from the first and secondmeasured signals are combined to form the primary reference. The primaryreference may then be used to remove the primary portion of each of thefirst and second measured signals via a correlation canceler. The outputof the correlation canceler is a good approximation to the secondarysignal with the primary signal removed and may be used for subsequentprocessing in the same instrument or an auxiliary instrument. In thiscapacity, the approximation to the secondary signal may be used as areference signal for input to a second correlation canceler togetherwith either the first or second measured signals for computation of,respectively, either the first or second primary signal portions.

[0017] Physiological monitors can often advantageously employ signalprocessors of the present invention. Often in physiological measurementsa first signal comprising a first primary portion and a first secondaryportion and a second signal comprising a second primary portion and asecond secondary portion are acquired. The signals may be acquired bypropagating energy through a patient's body (or a material which isderived from the body, such as breath, blood, or tissue, for example) orinside a vessel and measuring an attenuated signal after transmission orreflection. Alternatively, the signal may be acquired by measuringenergy generated by a patient's body, such as in electrocardiography.The signals are processed via the signal processor of the presentinvention to acquire either a secondary reference or a primary referencewhich is input to a correlation canceler, such as an adaptive noisecanceler.

[0018] One physiological monitoring apparatus which can advantageouslyincorporate the features of the present invention is a monitoring systemwhich determines a signal which is representative of the arterial pulse,called a plethysmographic wave. This signal can be used in bloodpressure calculations, blood gas saturation measurements, etc. Aspecific example of such a use is in pulse oximetry which determines thesaturation of oxygen in the blood. In this configuration, we define theprimary portion of the signal to be the arterial blood contribution toattenuation of energy as it passes through a portion of the body whereblood flows close to the skin. The pumping of the heart causes bloodflow to increase and decrease in the arteries in a periodic fashion,causing periodic attenuation wherein the periodic waveform is theplethysmographic waveform representative of the arterial pulse. Wedefine the secondary portion of the signal to be that which is usuallyconsidered to be noise. This portion of the signal is related to thevenous blood contribution to attenuation of energy as it passes throughthe body. Patient movement causes this component to flow in anunpredictable manner, causing unpredictable attenuation and corruptingthe otherwise periodic plethysmographic waveform. Respiration alsocauses secondary or noise component to vary, although typically at amuch lower frequency than the patients pulse rate.

[0019] A physiological monitor particularly adapted to pulse oximetryoxygen saturation measurement comprises two light emitting diodes(LED's) which emit light at different wavelengths to produce first andsecond signals. A detector registers the attenuation of the twodifferent energy signals after each passes through an absorptive media,for example a digit such as a finger, or an earlobe. The attenuatedsignals generally comprise both primary and secondary signal portions. Astatic filtering system, such as a bandpass filter, removes a portion ofthe secondary signal which is outside of a known bandwidth of interest,leaving an erratic or random secondary signal portion, often caused bymotion and often difficult to remove, along with the primary signalportion.

[0020] Next, a processor of the present invention removes the primarysignal portions from the measured signals yielding a secondary referencewhich is a combination of the remaining secondary signal portions. Thesecondary reference is correlated to both of the secondary signalportions. The secondary reference and at least one of the measuredsignals are input to a correlation canceler, such as an adaptive noisecanceler, which removes the random or erratic portion of the secondarysignal. This yields a good approximation to the primary plethysmographicsignal as measured at one of the measured signal wavelengths. As isknown in the art, quantitative measurements of the amount of oxygenatedarterial blood in the body can be determined from the plethysmographicsignal in a variety of ways.

[0021] The processor of the present invention may also remove thesecondary signal portions from the measured signals yielding a primaryreference which is a combination of the remaining primary signalportions. The primary reference is correlated to both of the primarysignal portions. The primary reference and at least one of the measuredsignals are input to a correlation canceler which removes the primaryportions of the measured signals. This yields a good approximation tothe secondary signal at one of the measured signal wavelengths. Thissignal may be useful for removing secondary signals from an auxiliaryinstrument as well as determining venous blood oxygen saturation.

[0022] One aspect of the present invention is a signal processorcomprising a detector for receiving a first signal which travels along afirst propagation path and a second signal which travels along a secondpropagation path wherein a portion of the first and second propagationpaths are located in a propagation medium. The first signal has a firstprimary signal portion and a first secondary signal portion and thesecond signal has a second primary signal portion and a second secondarysignal portion. The first and second secondary signal portions are aresult of a change of the propagation medium. This aspect of theinvention additionally comprises a reference processor having an inputfor receiving the first and second signals. The processor is adapted tocombine the first and second signals to generate a secondary referencehaving a significant component which is a function of the first and saidsecond secondary signal portions. The processor may also be adapted tocombine the first and second signals to generate a primary referencehaving a significant component which is a function of the first andsecond primary signal portions

[0023] The above described aspect of the present invention may furthercomprise a signal processor for receiving the secondary reference signaland the first signal and for deriving therefrom an output signal havinga significant component which is a function of the first primary signalportion of the first signal. Alternatively, the above described aspectof the present invention may further comprise a signal processor forreceiving the secondary reference signal and the second signal and forderiving therefrom an output signal having a significant component whichis a function of the second primary signal portion of the second signal.Alternatively, the above described aspect of the present invention mayfurther comprise a signal processor for receiving the primary referenceand the first signal and for deriving therefrom an output signal havinga significant component which is a function of the first secondarysignal portion of the signal of the first signal. Alternatively, theabove described aspect of the present invention may further comprise asignal processor for receiving the primary reference and the secondsignal and for deriving therefrom an output signal having a significantcomponent which is a function of the second secondary signal portion ofthe second signal. The signal processor may comprise a correlationcanceler, such as an adaptive noise canceler. The adaptive noisecanceler may comprise a joint process estimator having aleast-squares-lattice predictor and a regression filter.

[0024] The detector in the aspect of the signal processor of the presentinvention described above may further comprise a sensor for sensing aphysiological function. The sensor may comprise a light or otherelectromagnetic sensitive device. Additionally, the present inventionmay further comprise a pulse oximeter for measuring oxygen saturation ina living organism. The present invention may further comprise anelectrocardiograph.

[0025] Another aspect of the present invention is a physiologicalmonitoring apparatus comprising a detector for receiving a firstphysiological measurement signal which travels along a first propagationpath and a second physiological measurement signal which travels along asecond propagation path. A portion of the first and second propagationpaths being located in the same propagation medium. The first signal hasa first primary signal portion and a first secondary signal portion andthe second signal has a second primary signal portion and a secondsecondary signal portion. The physiological monitoring apparatus furthercomprises a reference processor having an input for receiving the firstand second signals. The processor is adapted to combine the first andsecond signals to generate a secondary reference signal having asignificant component which is a function of the first and the secondsecondary signal portions. Alternatively, the processor may be adaptedto combine the first and second signals to generate a primary referencehaving a component which is a function of the first and second primarysignal portions.

[0026] The physiological monitoring apparatus may further comprise asignal processor for receiving the secondary reference and the firstsignal and for deriving therefrom an output signal having a significantcomponent which is a function of the first primary signal portion of thefirst signal. Alternatively, the physiological monitoring apparatus mayfurther comprise a signal processor for receiving the secondaryreference and the second signal and for deriving therefrom an outputsignal having a significant component which is a function of the secondprimary signal portion of the second signal. Alternatively, thephysiological monitoring apparatus may further comprise a signalprocessor for receiving the primary reference and the first signal andderiving therefrom an output signal having a significant component whichis a function of the first secondary signal portion of the first signal.Alternatively, the physiological monitoring apparatus may furthercomprise a signal processor for receiving the primary reference and thesecond signal and deriving therefrom an output signal having asignificant component which is a function of the second secondary signalportion of the second signal.

[0027] A further aspect of the present invention is an apparatus formeasuring a blood constituent comprising an energy source for directinga plurality of predetermined wavelengths of electromagnetic energy upona specimen and a detector for receiving the plurality of predeterminedwavelengths of electromagnetic energy from the specimen. The detectorproduces electrical signals corresponding to the predeterminedwavelengths in response to the electromagnetic energy. At least two ofthe electrical signals are used each having a primary signal portion andan secondary signal portion. Additionally, the apparatus comprises areference processor having an input for receiving the electricalsignals. The processor is configured to combine said electrical signalsto generate a secondary reference having a significant component whichis derived from the secondary signal portions. Alternatively, theprocessor may be configured to combine said signals to generate aprimary reference having a significant component which is derived fromthe primary signal portions.

[0028] This aspect of the present invention may further comprise asignal processor for receiving the secondary reference and one of thetwo electrical signals and for deriving therefrom an output signalhaving a significant component which is a function of the primary signalportion of one of the two electrical signals. Another aspect of thepresent invention may further comprise a signal processor for receivingthe primary reference and one of the two electrical signals and forderiving therefrom an output signal having a significant component whichis a function of the secondary signal portion of one of the twoelectrical signals. This may be accomplished by use of a correlationcanceler, such as an adaptive noise canceler, in the signal processorwhich may employ a joint process estimator having aleast-squares-lattice predictor and a regression filter.

[0029] Yet another aspect of the present invention is a blood gasmonitor for non-invasively measuring a blood constituent in a bodycomprising a light source for directing at least two predeterminedwavelengths of light upon a body and a detector for receiving the lightfrom the body. The detector, in response to the light from the body,produces at least two electrical signals corresponding to the at leasttwo predetermined wavelengths of light. The at least two electricalsignals each have a primary signal portion and a secondary signalportion. The blood oximeter further comprises a reference processorhaving an input for receiving the at least two electrical signals. Theprocessor is adapted to combine the at least two electrical signals togenerate a secondary reference with a significant component which isderived from the secondary signal portions. The blood oximeter mayfurther comprise a signal processor for receiving the secondaryreference and the two electrical signals and for deriving therefrom atleast two output signals which are substantially equal, respectively, tothe primary signal portions of the electrical signals. Alternatively,the reference processor may be adapted to combine the at least twoelectrical signals to generate a primary reference with a significantcomponent which is derived from the primary signal portions. The bloodoximeter may further comprise a signal processor for receiving theprimary reference and the two electrical signals and for derivingtherefrom at least two output signals which are substantially equivalentto the secondary signal portions of the electrical signal. The signalprocessor may comprise a joint process estimator.

[0030] The present invention also includes a method of determining asecondary reference from a first signal comprising a first primarysignal portion and a first secondary portion and a second signalcomprising a second primary signal portion and a second secondaryportion. The method comprises the steps of selecting a signalcoefficient which is proportional to a ratio of predetermined attributesof the first primary signal portion and predetermined attributes of thesecond primary signal portion. The first signal and the signalcoefficient are input into a signal multiplier wherein the first signalis multiplied by the signal coefficient thereby generating a firstintermediate signal. The second signal and the first intermediate signalare input into a signal subtractor wherein the first intermediate signalis subtracted from the second signal. This generates a secondaryreference having a significant component which is derived from the firstand second secondary signal portions.

[0031] The present invention also includes a method of determining aprimary reference from a first signal comprising a first primary signalportion and a first secondary signal portion and a second signalcomprising a second primary signal portion and a second secondary signalportion. The method comprises the steps of selecting a signalcoefficient which is proportional to a ratio of the predeterminedattributes of the first secondary signal portion and predeterminedattributes of the second secondary signal portion. The first signal andthe signal coefficient are input into a signal multiplier wherein thefirst signal is multiplied by the signal coefficient thereby generatinga first intermediate signal. The second signal and the firstintermediate signal are input into a signal subtractor wherein the firstintermediate signal is subtracted from the second signal. This generatesa primary reference having a significant component which is derived fromthe first and second primary signal portions. The first and secondsignals in this method may be derived from electromagnetic energytransmitted through an absorbing medium.

[0032] The present invention further embodies a physiological monitoringapparatus comprising means for acquiring a first signal comprising afirst primary signal portion and a first secondary signal portion and asecond signal comprising a second primary signal portion and a secondsecondary signal portion. The physiological monitoring apparatus of thepresent invention also comprises means for determining from the firstand second signals a secondary reference. Additionally, the monitoringapparatus comprises a correlation canceler, such as an adaptive noisecanceler, having a secondary reference input for receiving the secondaryreference and a signal input for receiving the first signal wherein thecorrelation canceler, in real or near real time, generates an outputsignal which approximates the first primary signal portion.Alternatively, the physiological monitoring device may also comprisemeans for determining from the first and second signals a primaryreference. Additionally, the monitoring apparatus comprises acorrelation canceler having a primary reference input for receiving theprimary reference and a signal input for receiving the first signalwherein the correlation canceler, in real or near real time, generatesan output signal which approximates the first secondary signal portion.The correlation canceler may further comprise a joint process estimator.

[0033] A further aspect of the present invention is an apparatus forprocessing an amplitude modulated signal having a signal amplitudecomplicating feature, the apparatus comprising an energy source fordirecting electromagnetic energy upon a specimen. Additionally, theapparatus comprises a detector for acquiring a first amplitude modulatedsignal and a second amplitude modulated signal. Each of the first andsecond signals has a component containing information about theattenuation of electromagnetic energy by the specimen and a signalamplitude complicating feature. The apparatus includes a referenceprocessor for receiving the first and second amplitude modulated signalsand deriving therefrom a secondary reference which is correlated withthe signal amplitude complicating feature. Further, the apparatusincorporates a correlation canceler having a signal input for receivingthe first amplitude modulated signal, a secondary reference input forreceiving the secondary reference, wherein the correlation cancelerproduces an output signal having a significant component which isderived from the component containing information about the attenuationof electromagnetic energy by the specimen. Alternatively, the apparatusmay also include a reference processor for receiving the first andsecond amplitude modulated signals and deriving therefrom a primaryreference which is correlated with the component containing informationabout the attenuation of electromagnetic energy by the specimen.Further, the apparatus incorporates a correlation canceler having asignal input for receiving the first amplitude modulated signal, aprimary reference input for receiving the primary reference, wherein thecorrelation canceler produces an output signal having a primarycomponent which is derived from the signal amplitude complicatingfeature.

[0034] Still another aspect of the present invention is an apparatus forextracting a plethysmographic waveform from an amplitude modulatedsignal having a signal amplitude complicating feature, the apparatuscomprising a light source for transmitting light into an organism and adetector for monitoring light from the organism. The detector produces afirst light attenuation signal and a second light attenuation signal,wherein each of the first and second light attenuation signals has acomponent which is representative of a plethysmographic waveform and acomponent which is representative of the signal amplitude complicatingfeature. The apparatus also includes a reference processor for receivingthe first and second light attenuation signals and deriving therefrom asecondary reference. The secondary reference and the signal amplitudecomplicating feature each have a frequency spectrum. The frequencyspectrum of the secondary reference is correlated with the frequencyspectrum of the signal amplitude complicating feature. Additionallyincorporated into this embodiment of the present invention is acorrelation canceler having a signal input for receiving the firstattenuation signal and a secondary reference input for receiving thesecondary reference. The correlation canceler produces an output signalhaving a significant component which is derived from the component whichis representative of a plethysmographic waveform. The apparatus may alsoinclude a reference processor for receiving the first and second lightattenuation signals and deriving therefrom a primary reference.Additionally incorporated in this embodiment of the present invention isa correlation canceler having a signal input for receiving the firstattenuation signal and a primary reference input for receiving theprimary reference. The correlation canceler produces an output signalhaving a significant component which is derived from the component whichis representative of the signal complicating feature.

[0035] The present invention also comprises a method of removing ordetermining a motion artifact signal from a signal derived from aphysiological measurement wherein a first signal having a physiologicalmeasurement component and a motion artifact component and a secondsignal having a physiological measurement component and a motionartifact component are acquired. From the first and second signals asecondary reference which is a primary function of the first and secondsignals motion artifact components is derived. This method of removing amotion artifact signal from a signal derived from a physiologicalmeasurement may also comprise the step of inputting the secondaryreference into a correlation canceler, such as an adaptive noisecanceler, to produce an output signal which is a significant function ofthe physiological measurement component of the first or second signal.Alternatively, from the first and second signals a primary referencewhich is a significant function of the physiological measurementcomponents of the first and second signals may be derived. This approachmay also comprise the step of inputting the primary reference into acorrelation canceler to produce an output signal which is a significantfunction of the first or second signal's motion artifact component.

BRIEF DESCRIPTION OF THE DRAWINGS

[0036]FIG. 1 illustrates an ideal plethysmographic waveform.

[0037]FIG. 2 schematically illustrates the cross-sectional structure ofa typical finger.

[0038]FIG. 3 illustrates a plethysmographic waveform which includes amotion-induced erratic signal portion.

[0039]FIG. 4a illustrates a schematic diagram of a physiologicalmonitor, to compute primary physiological signals, incorporating aprocessor of the present invention, and a correlation canceler.

[0040]FIG. 4b illustrates a schematic diagram of a physiologicalmonitor, to compute secondary erratic signals, incorporating a processorof the present invention, and a correlation canceler.

[0041]FIG. 5a illustrates an example of an adaptive noise canceler whichcould be employed in a physiological monitor, to compute primaryphysiological signals, which also incorporates the processor of thepresent invention.

[0042]FIG. 5b illustrates an example of an adaptive noise canceler whichcould be employed in a physiological monitor, to compute secondarymotion artifact signals, which also incorporates the processor of thepresent invention.

[0043]FIG. 5c illustrates the transfer function of a multiple notchfilter.

[0044]FIG. 6a illustrates a schematic absorbing material comprising Nconstituents within an absorbing material.

[0045]FIG. 6b illustrates another schematic absorbing materialcomprising N constituents, including one mixed layer, within anabsorbing material.

[0046]FIG. 6c illustrates another schematic absorbing materialcomprising N constituents, including two mixed layers, within anabsorbing material.

[0047]FIG. 7a illustrates a schematic diagram of a monitor, to computeprimary and secondary signals, incorporating a processor of the presentinvention, a plurality of signal coefficients ω₁, ω₂, . . . ω_(n), and acorrelation canceler.

[0048]FIG. 7b illustrates the ideal correlation canceler energy or poweroutput as a function of the signal coefficients ω₁, ω₂, . . . ω_(n). Inthis particular example, ω₃=ω_(a) and ω₇=ω_(v).

[0049]FIG. 7c illustrates the non-ideal correlation canceler energy orpower output as a function of the signal coefficients ω₁, ω₂, . . .ω_(n). In this particular example, ω₃=ω_(a) and ω₇=ω_(v).

[0050]FIG. 8 is a schematic model of a joint process estimatorcomprising a least-squares lattice predictor and a regression filter.

[0051]FIG. 9 is a flowchart representing a subroutine capable ofimplementing a joint process estimator as modeled in FIG. 8.

[0052]FIG. 10 is a schematic model of a joint process estimator with aleast-squares lattice predictor and two regression filters.

[0053]FIG. 11 is an example of a physiological monitor incorporating aprocessor of the present invention and a correlation canceler within amicroprocessor. This physiological monitor is specifically designed tomeasure a plethysmographic waveform or a motion artifact waveform andperform oximetry measurements.

[0054]FIG. 12 is a graph of oxygenated and deoxygenated hemoglobinabsorption coefficients vs. wavelength.

[0055]FIG. 13 is a graph of the ratio of the absorption coefficients ofdeoxygenated hemoglobin divided by oxygenated hemoglobin vs. wavelength.

[0056]FIG. 14 is an expanded view of a portion of FIG. 12 marked by acircle labeled 13.

[0057]FIG. 15 illustrates a signal measured at a first red wavelengthλa=λred1=650 nm for use in a processor of the present inventionemploying the ratiometric method for determining either the primaryreference n′(t) or the secondary reference s′(t) and for use in acorrelation canceler, such as an adaptive noise canceler. The measuredsignal comprises a primary portion s_(λa)(t) and a secondary portionn_(λa)(t).

[0058]FIG. 16 illustrates a signal measured at a second red wavelengthλb=λred2=685 nm for use in a processor of the present inventionemploying the ratiometric method for determining the secondary referencen′(t) or the primary reference s′(t). The measured signal comprises aprimary portion s_(λb)(t) and a secondary portion n_(λb)(t).

[0059]FIG. 17 illustrates a signal measured at an infrared wavelengthλc=λIR=940 nm for use in a correlation canceler. The measured signalcomprises a primary portion s_(λc)(t) and a secondary portion n_(λc)(t).

[0060]FIG. 18 illustrates the secondary reference n′(t) determined by aprocessor of the present invention using the ratiometric method.

[0061]FIG. 19 illustrates the primary reference s′(t) determined by aprocessor of the present invention using the ratiometric method.

[0062]FIG. 20 illustrates a good approximation s″_(λa)(t) to the primaryportion s_(λa)(t) of the signal S_(λa)(t) measured at λa=λred1=650 nmestimated by correlation cancellation with a secondary reference n′(t)determined by the ratiometric method.

[0063]FIG. 21 illustrates a good approximation s″_(λc)(t) to the primaryportion s_(λc)(t) of the signal S_(λc)(t) measured at λc=λIR=940 nmestimated by correlation cancellation with a secondary reference n′(t)determined by the ratiometric method.

[0064]FIG. 22 illustrates a good approximation n″_(λa)(t) to thesecondary portion n_(λa)(t) of the signal S_(λa)(t) measured atλa=λred1=650 nm estimated by correlation cancellation with a primaryreference s′(t) determined by the ratiometric method.

[0065]FIG. 23 illustrates a good approximation n″_(λc)(t) to thesecondary portion n_(λc)(t) of the signal S_(λc)(t) measured atλc=λIR=940 nm estimated by correlation cancelation with a primaryreference s′(t) determined by the ratiometric method.

[0066]FIG. 24 illustrates a signal measured at a red wavelengthλa=λred=660 nm for use in a processor of the present invention employingthe constant saturation method for determining the secondary referencen′(t) or the primary reference s′(t) and for use in a correlationcanceler. The measured signal comprises a primary portion s_(λa)(t) anda secondary portion n_(λa)(t).

[0067]FIG. 25 illustrates a signal measured at an infrared wavelengthλb=λIR=940 nm for use in a processor of the present invention employingthe constant saturation method for determining the secondary referencen′(t) or the primary reference s′(t) and for use in a correlationcanceler. The measured signal comprises a primary portion s_(λb)(t) anda secondary portion n_(λb)(t).

[0068]FIG. 26 illustrates the secondary reference n′(t) determined by aprocessor of the present invention using the constant saturation method.

[0069]FIG. 27 illustrates the primary reference s′(t) determined by aprocessor of the present invention using the constant saturation method.

[0070]FIG. 28 illustrates a good approximation S″_(λa)(t) to the primaryportion s_(λa)(t) of the signal S_(λa)(t) measured at λa=λred=660 nmestimated by correlation cancelation with a secondary reference n′(t)determined by the constant saturation method.

[0071]FIG. 29 illustrates a good approximation s″_(λb)(t) to the primaryportion s_(λb)(t) of the signal S_(λb)(t) measured at λb=λIR=940 nmestimated by correlation cancelation with a secondary reference n′(t)determined by the constant saturation method.

[0072]FIG. 30 illustrates a good approximation n″_(λa)(t) to thesecondary portion n_(λa)(t) of the signal S_(λa)(t) measured atλa=λred=660 nm estimated by correlation cancelation with a primaryreference s′(t) determined by the constant saturation method.

[0073]FIG. 31 illustrates a good approximation n″_(λb)(t) to thesecondary portion n_(λb)(t) of the signal S_(λb)(t) measured atλb=λIR=940 nm estimated by correlation cancelation with a primaryreference s′(t) determined by the constant saturation method.

[0074]FIG. 32 depicts a set of 3 concentric electrodes, i.e. a tripolarelectrode sensor, to derive electrocardiography (ECG) signals, denotedas S₁, S₂ and S₃, for use with the present invention. Each of the ECGsignals contains a primary portion and a secondary portion.

DETAILED DESCRIPTION OF THE INVENTION

[0075] The present invention is a processor which determines either asecondary reference n′(t) or a primary reference s′(t) for use in acorrelation canceler, such as an adaptive noise canceler. A correlationcanceler may estimate a good approximation s″(t) to a primary signals(t) from a composite signal S(t)=s(t)+n(t) which, in addition to theprimary portion s(t) comprises a secondary portion n(t). It may also beused to provide a good approximation n″(t) to the secondary signal n(t).The secondary portion n(t) may contain one or more of a constantportion, a predictable portion, an erratic portion, a random portion,etc. The approximation to the primary signal s″(t) or secondary signaln″(t) is derived by removing as many of the secondary portions n(t) orprimary portions s(t) from the composite signal S(t) as possible. Theconstant portion and predictable portion are easily removed withtraditional filtering techniques, such as simple subtraction, low pass,band pass, and high pass filtering. The erratic portion is moredifficult to remove due to its unpredictable nature. If something isknown about the erratic signal, even statistically, it could be removed,at least partially, from the measured signal via traditional filteringtechniques. However, it is often the case that no information is knownabout the erratic portion of the noise. In this case, traditionalfiltering techniques are usually insufficient. Often no informationabout the erratic portion of the measured signal is known. Thus, acorrelation canceler, such as an adaptive noise canceler may be utilizedin the present invention to remove or derive the erratic portion.

[0076] Generally, a correlation canceler has two signal inputs and oneoutput. One of the inputs is either the secondary reference n′(t) or theprimary reference s′(t) which are correlated, respectively, to thesecondary signal portions n(t) and the primary signal portions s(t)present in the composite signal S(t). The other input is for thecomposite signal S(t). Ideally, the output of the correlation cancelers″(t) or n″(t) corresponds, respectively, to the primary signal s(t) orthe secondary signal n(t) portions only. Often, the most difficult taskin the application of correlation cancelers is determining the referencesignals n′(t) and s′(t) which are correlated to the secondary n(t) andprimary s(t) portions, respectively, of the measured signal S(t) since,as discussed above, these portions are quite difficult to isolate fromthe measured signal S(t). In the signal processor of the presentinvention, either a secondary reference n′(t) or a primary references′(t) is determined from two composite signals measured simultaneously,or nearly simultaneously, at two different wavelengths, λa and λb.

[0077] A block diagram of a generic monitor incorporating a signalprocessor, or reference processor, according to the present invention,and a correlation canceler is shown in FIGS. 4a and 4 b. Two measuredsignals, S_(λa)(t) and S_(λb)(t), are acquired by a detector 20. Oneskilled in the art will realize that for some physiologicalmeasurements, more than one detector may be advantageous. Each signal isconditioned by a signal conditioner 22 a and 22 b. Conditioningincludes, but is not limited to, such procedures as filtering thesignals to remove constant portions and amplifying the signals for easeof manipulation. The signals are then converted to digital data by ananalog-to-digital converter 24 a and 24 b. The first measured signalS_(λa)(t) comprises a first primary signal portion, labeled hereins_(λa)(t), and a first secondary signal portion, labeled hereinn_(λa)(t). The second measured signal S_(λb)(t) is at least partiallycorrelated to the first measured signal S_(λa)(t) and comprises a secondprimary signal portion, labeled herein s_(λb)(t), and a second secondarysignal portion, labeled herein n_(λb)(t). Typically the first and secondsecondary signal portions, n_(λa)(t) and n_(λb)(t), are uncorrelatedand/or erratic with respect to the primary signal portions s_(λa)(t) ands_(λb)(t). The secondary signal portions n_(λa)(t) and n_(λb)(t) areoften caused by motion of a patient. The signals S_(λa)(t) and S_(λb)(t)are input to a reference processor 26. The reference processor 26multiplies the second measured signal S_(λb)(t) by either a factorω_(a)=s_(λa)(t)/s_(λb)(t) or a factor ω_(v)=n_(λa)(t)/n_(λb)(t) and thensubtracts the second measured signal S_(λb)(t) from the first measuredsignal S_(λa)(t). The signal coefficient factors ω_(a) and ω_(λv) aredetermined to cause either the primary signal portions s_(λa)(t) ands_(λb)(t) or the secondary signal portions n_(λa)(t) and n_(λb)(t) tocancel when the two signals S_(λa)(t) and S_(λb)(t) are subtracted.Thus, the output of the reference processor 26 is either a secondaryreference signal n′(t)=n_(λa)(t)−ω_(a)n_(λb)(t), in FIG. 4a, which iscorrelated to both of the secondary signal portions n_(λa)(t) andn_(λb)(t) or a primary reference signal s′(t)=s_(λa)(t)−ω_(v)s_(λb)(t),in FIG. 4b, which is correlated to both of the primary signal portionss_(λa)(t) and s_(λb)(t). A reference signal n′(t) or s′(t) is input,along with one of the measured signals S_(λa)(t) or S_(λb)(t), to acorrelation canceler 27 which uses the reference signal n′(t) or s′(t)to remove either the secondary signal portions n_(λa)(t) or n_(λb)(t) orthe primary signal portions s_(λa)(t) or s_(λb)(t) from the measuredsignal S_(λa)(t) or S_(λb)(t). The output of the correlation canceler 27is a good approximation s″(t) or n″(t) to either the primary s(t) or thesecondary n(t) signal components. The approximation s″(t) or n″(t) isdisplayed on the display 28.

[0078] An adaptive noise canceler 30, an example of which is shown inblock diagram form in FIG. 5a, is employed to remove either one of theerratic, secondary signal portions n_(λa)(t) and n_(λb)(t) from thefirst and second signals _(λa)(t) and S_(λb)(t). The adaptive noisecanceler 30, which performs the functions of a correlation canceler, inFIG. 5a has as one input a sample of the secondary reference n′(t) whichis correlated to the secondary signal portions n_(λa)(t) and n_(λb)(t).The secondary reference n′(t) is determined from the two measuredsignals S_(λa)(t) and S_(λb)(t) by the processor 26 of the presentinvention as described herein. A second input to the adaptive noisecanceler, is a sample of either the first or second composite measuredsignals S_(λa)(t)=s_(λa)(t)+n_(λa)(t) or S_(λb)(t)=s_(λb)(t)+n_(λb)(t).

[0079] The adaptive noise canceler 30, in FIG. 5b, may also be employedto remove either one of primary signal portions s_(λa)(t) and s_(λb)(t)from the first and second signals S_(λa)(t) and S_(λb)(t). The adaptivenoise canceler 30 has as one input a sample of the primary references′(t) which is correlated to the primary signal portions s_(λa)(t) ands_(λb)(t). The primary reference s′(t) is determined from the twomeasured signals S_(λa)(t) and S_(λb)(t) by the processor 26 of thepresent invention as described herein. A second input to the adaptivenoise canceler 30 is a sample of either the first or second measuredsignals S_(λa)(t)=s_(λa)(t)+n_(λa)(t) or S_(λb)(t)=s_(λb)(t)+n_(λb)(t).

[0080] The adaptive noise canceler 30 functions to remove frequenciescommon to both the reference n′(t) or s′(t) and the measured signalS_(λa)(t) or S_(λb)(t). Since the reference signals are correlated toeither the secondary signal portions n_(λa)(t) and n_(λb)(t) or theprimary signal portions s_(λa)(t) and s_(λb)(t), the reference signalswill be correspondingly erratic or well behaved. The adaptive noisecanceler 30 acts in a manner which may be analogized to a dynamicmultiple notch filter based on the spectral distribution of thereference signal n′(t) or s′(t).

[0081] Referring to FIG. 5c, the transfer function of a multiple notchfilter is shown. The notches, or dips in the amplitude of the transferfunction, indicate frequencies which are attenuated or removed when acomposite measured signal passes through the notch filter. The output ofthe notch filter is the composite signal having frequencies at which anotch was present removed. In the analogy to an adaptive noise canceler30, the frequencies at which notches are present change continuouslybased upon the inputs to the adaptive noise canceler 30.

[0082] The adaptive noise canceler 30 shown in FIGS. 5a and 5 b producesan output signal, labeled herein as S″_(λa)(t), s″_(λb)(t), n″_(λa)(t)or n″_(λb)(t) which is fed back to an internal processor 32 within theadaptive noise canceler 30. The internal processor 32 automaticallyadjusts its own transfer function according to a predetermined algorithmsuch that the output of the internal processor 32, labeled b(t) in FIG.5a or c(t) in FIG. 5b, closely resembles either the secondary signalportion n_(λa)(t) or n_(λb)(t) or the primary signal portion s_(λa)(t)or s_(λb)(t). The output b(t) of the internal processor 32 in FIG. 5a issubtracted from the measured signal, S_(80 a)(t) or S_(λb)(t), yieldinga signal output s″_(λa)(t)=s_(λa)(t)+n_(λa)(t)−b_(λa)(t) or a signaloutput S″_(λb)(t)=s_(λb)(t)+n_(λb)(t)−b_(λb)(t). The internal processoroptimizes s″_(λa)(t) or s″_(λb)(t) such that s″_(λa)(t) or s″_(λb)(t) isapproximately equal to the primary signal s_(λa)(t) or s_(λb)(t),respectively. The output c(t) of the internal processor 32 in FIG. 5b issubtracted from the measured signal, S_(λa)(t) or S_(λb)(t), yielding asignal output given by n″_(λa)(t)=s_(λa)(t)+n_(λa)(t)−c_(λa)(t) or asignal output given by n″_(λb)(t)=s_(b)(t)+n″_(λb)(t)−c_(λb)(t). Theinternal processor optimizes n″_(λa)(t) or n″_(λb)(t) such thatn″_(λa)(t) or n″_(λb)(t) is approximately equal to the secondary signaln_(λa)(t) or n_(λb)(t), respectively.

[0083] One algorithm which may be used for the adjustment of thetransfer function of the internal processor 32 is a least-squaresalgorithm, as described in Chapter 6 and Chapter 12 of the book AdaptiveSignal Processing by Bernard Widrow and Samuel Stearns, published byPrentice Hall, copyright 1985. This entire book, including Chapters 6and 12, is hereby incorporated herein by reference.

[0084] Adaptive processors 30 in FIGS. 5a and 5 b have been successfullyapplied to a number of problems including antenna. sidelobe canceling,pattern recognition, the elimination of periodic interference ingeneral, and the elimination of echoes on long distance telephonetransmission lines. However, considerable ingenuity is often required tofind a suitable reference signal n′(t) or s′(t) since the portionsn_(λa)(t), n_(λb)(t), s_(λa)(t) and s_(λb)(t) cannot easily be separatedfrom the measured signals S_(λa)(t) and S_(λb)(t). If either the actualsecondary portion n_(λa)(t) or n_(λb)(t) or the primary signal portions_(λa)(t) or s_(λb)(t) were a priori available, techniques such ascorrelation cancellation would not be necessary. The determination of asuitable reference signal n′(t) or s′(t) from measurements taken by amonitor incorporating a reference processor of the present invention isone aspect of the present invention.

Generalized Determination of Primary and Secondary Reference Signals

[0085] An explanation which describes how the reference signals n′(t)and s′(t) may be determined follows. A first signal is measured at, forexample, a wavelength λa, by a detector yielding a signal S_(λa)(t):

S _(λa)(t)=s _(λa)(t)+n _(λa)(t)   (1)

[0086] where s_(λa)(t) is the primary signal and n_(λa)(t) is thesecondary signal.

[0087] A similar measurement is taken simultaneously, or nearlysimultaneously, at a different wavelength, λb; yielding:

S _(λb)(t)=s _(λb)(t)+n _(λb)(t).   (2)

[0088] Note that as long as the measurements, S_(λa)(t) and S_(λb)(t),are taken substantially simultaneously, the secondary signal components,n_(λa)(t) and n_(λb)(t), will be correlated because any random orerratic functions will affect each measurement in nearly the samefashion. The well behaved primary signal components, s_(λa)(t) ands_(λb)(t), will also be correlated to one another.

[0089] To obtain the reference signals n′(t) and s′(t), the measuredsignals S_(λa)(t) and S_(λb)(t) are transformed to eliminate,respectively, the primary or secondary signal components. One way ofdoing this is to find proportionality constants, ω_(a) and ω_(v),between the primary signals s_(λa)(t) and s_(λb)(t) and secondarysignals n_(λa)(t) and n_(λb)(t) such that:

s _(λa)(t)=ω_(a) s _(λb)(t)

n _(λa)(t)=ω_(v) n _(λb)(t).   (3)

[0090] These proportionality relationships can be satisfied in manymeasurements, including but not limited to absorption measurements andphysiological measurements. Additionally, in most measurements, theproportionality constants ω_(a) and ω_(v) can be determined such that:

n _(λa)(t)≠ω_(a) n _(λb)(t)

s _(λa)(t)≠ω_(v) s _(λb)(t).   (4)

[0091] Multiplying equation (2) by ω_(a) and then subtracting equation(2) from equation (1) results in a single equation wherein the primarysignal terms s_(λa)(t) and s_(λb)(t) cancel, leaving:

n′(t)=S _(λa)(t)−ω_(a) S _(λb)(t)=n _(λa)(t)−ω_(a) n _(λb)(t);   (5a)

[0092] a non-zero signal which is correlated to each secondary signalportion n_(λa)(t) and n_(λb)(t) and can be used as the secondaryreference n′(t) in a correlation canceler such as an adaptive noisecanceler.

[0093] Multiplying equation (2) by ω_(v) and then subtracting equation(2) from equation (1) results in a single equation wherein the secondarysignal terms n_(λa)(t) and n_(nλb)(t) cancel, leaving:

s′(t)=S _(λa)(t)−ω_(v) S _(λb)(t)=s _(λa)(t)−ω_(v) s _(λb)(t);   (5b)

[0094] a non-zero signal which is correlated to each of the primarysignal portions s_(λa)(t) and s_(λb)(t) and can be used as the signalreference s′(t) in a correlation canceler such as an adaptive noisecanceler.

Example of Determination of Primary and Secondary Reference Signals inan Absorptive System

[0095] Correlation canceling is particularly useful in a large number ofmeasurements generally described as absorption measurements. An exampleof an absorption type monitor which can advantageously employcorrelation canceling, such as adaptive noise canceling, based upon areference n′(t) or s′(t) determined by a processor of the presentinvention is one which determines the concentration of an energyabsorbing constituent within an absorbing material when the material issubject to change. Such changes can be caused by forces about whichinformation is desired or primary, or alternatively, by random orerratic secondary forces such as a mechanical force on the material.Random or erratic interference, such as motion, generates secondarycomponents in the measured signal. These secondary components can beremoved or derived by the correlation canceler if a suitable secondaryreference n′(t) or primary reference s′(t) is known.

[0096] A schematic N constituent absorbing material comprising acontainer 42 having N different absorbing constituents, labeled A₁, A₂,A₃, . . . A_(N), is shown schematically in FIG. 6a. The constituents A₁through A_(N) in FIG. 6a are arranged in a generally orderly, layeredfashion within the container 42. An example of a particular type ofabsorptive system is one in which light energy passes through thecontainer 42 and is absorbed according to the generalized Beer-LambertLaw of light absorption. For light of wavelength λa, this attenuationmay be approximated by:

I=I _(O)exp(−Σ^(N) _(i=1)ε_(i, λa) c _(i) x _(i))   (6)

[0097] Initially transforming the signal by taking the natural logarithmof both sides and manipulating terms, the signal is transformed suchthat the signal components are combined by addition rather thanmultiplication, i.e.:

S _(λa) =ln(I _(O) /I)=Σ^(N) _(i=1)ε_(i, λa) c _(i) x _(i)   (7)

[0098] where I_(O) is the incident light energy intensity; I is thetransmitted light energy intensity; ε_(i,λa) is the absorptioncoefficient of the i^(th) constituent at the wavelength λa; x_(i)(t) isthe optical path length of i^(th) layer, i.e., the thickness of materialof the i^(th) layer through which optical energy passes; and c_(i)(t) isthe concentration, of the i^(th) constituent in the volume associatedwith the thickness x_(i)(t). The absorption coefficients ε₁ throughε_(N) are known values which are constant at: each wavelength. Mostconcentrations c₁(t) through c_(N)(t) are typically unknown, as are mostof the optical path lengths x_(i)(t) of each layer. The total opticalpath length is the sum of each of the individual optical path lengthsx_(i)(t) of each layer.

[0099] When the material is not subject to any forces which cause changein the thicknesses of the layers, the optical path length of each layer,x_(i)(t), is generally constant. This results in generally constantattenuation of the optical energy and thus, a generally constant offsetin the measured signal. Typically, this portion of the signal is oflittle interest since knowledge about a force which perturbs thematerial is usually desired. Any signal portion outside of a knownbandwidth of interest, including the constant undesired signal portionresulting from the generally constant absorption of the constituentswhen not subject to change, should be removed. This is easilyaccomplished by traditional band pass filtering techniques. However,when the material is subject to forces, each layer of constituents maybe affected by the perturbation differently than each other layer. Someperturbations of the optical path lengths of each layer x_(i)(t) mayresult in excursions in the measured signal which represent desired orprimary information. Other perturbations of the optical path length ofeach layer x_(i)(t) cause undesired or secondary excursions which maskprimary information in the measured signal. Secondary signal componentsassociated with secondary excursions must also be removed to obtainprimary information from the measured signal. Similarly, the ability tocompute secondary signal components caused by secondary excursionsdirectly allows one to obtain primary signal components from themeasured signal via simple subtraction, or correlation cancellationtechniques.

[0100] The correlation canceler may selectively remove from thecomposite signal, measured after being transmitted through or reflectedfrom the absorbing material, either the secondary or the primary signalcomponents caused by forces which perturb or change the materialdifferently from the forces which perturbed or changed the material tocause respectively, either the primary or secondary signal component.For the purposes of illustration, it will be assumed that the portion ofthe measured signal which is deemed to be the primary signal s_(λa)(t)is the attenuation term ε₅c₅x₅(t) associated with a constituent ofinterest, namely A₅, and that the layer of constituent A₅ is affected byperturbations different than each of the layers of other constituents A₁through A₄ and A₆ through A_(N). An example of such a situation is whenlayer A₅ is subject to forces about which information is deemed to beprimary and, additionally, the entire material is subject to forceswhich affect each of the layers. In this case, since the total forceaffecting the layer of constituent A₅ is different than the total forcesaffecting each of the other layers and information is deemed to beprimary about the forces and resultant perturbation of the layer ofconstituent A₅, attenuation terms due to constituents A₁ through A₄ andA₆ through A_(N) make up the secondary signal portion n_(λa)(t). Even ifthe additional forces which affect the entire material cause the sameperturbation in each layer, including the layer of A₅, the total forceson the layer of constituent A₅ cause it to have different totalperturbation than each of the other layers of constituents A₁ through A₄and A₆ through A_(N).

[0101] It is often the case that the total perturbation affecting thelayers associated with the secondary signal components is caused byrandom or erratic forces. This causes the thickness of layers to changeerratically and the optical path length of each layer, x_(i)(t), tochange erratically, thereby producing a random or erratic secondarysignal component n_(λa)(t). However, regardless of whether or not thesecondary signal portion n_(λa)(t) is erratic, the secondary signalcomponent n_(λa)(t) can be either removed or derived via a correlationcanceler, such as an adaptive noise canceler, having as one input,respectively, a secondary reference n′(t) or a primary reference s′(t)determined by a processor of the present invention as long as theperturbation on layers other than the layer of constituent A₅ isdifferent than the perturbation on the layer of constituent A₅. Thecorrelation canceler yields a good approximation to either the primarysignal s_(λa)(t) or the secondary signal n_(λa)(t). In the event that anapproximation to the primary signal is obtained, the concentration ofthe constituent of interest, c₅(t), can often be determined since insome physiological measurements, the thickness of the primary signalcomponent, x₅(t) in this example, is known or can be determined.

[0102] The correlation canceler utilized a sample of either thesecondary reference n′(t) or the primary reference s′(t) determined fromtwo substantially simultaneously measured signals S_(λa)(t) andS_(λb)(t). S_(λa)(t) is determined as above in equation (7). S_(λb)(t)is determined similarly at a different wavelength λb. To find either thesecondary reference n′(t) or the primary reference s′(t), attenuatedtransmitted energy is measured at the two different wavelengths λa andλb and transformed via logarithmic conversion. The signals S_(λa)(t) andS_(λb)(t) can then be written (logarithm converted) as:

S _(λa)(t)=ε_(5,λa) c ₅ x ₅(t)+Σ⁴ _(i=1)ε_(i,λa) c _(i) x _(i)+Σ^(N)_(i=6)ε_(i,λa) c _(i) x _(i)   (8)

S _(λa)(t)=ε_(5,λa) c ₅ x ₅(t)+n _(λa)(t)   (9)

S _(λb)(t)=ε_(5,λb) c ₅ x ₅(t)+Σ⁴ _(i=1)ε_(i,λb) c _(i) x _(i)+Σ^(N)_(i=6)ε_(i,λb) c _(i) x _(i)   (10)

S _(λb)(t)=ε_(5,λb) c ₅ x ₅(t)+n _(λb)(t)   (11)

[0103] Further transformations of the signals are the proportionalityrelationships defining ω_(a) and ω_(v), similarly to equation (3), whichallows determination of a noise reference n′(t) and a primary references′(t). These are:

ε_(5, λa)=ω_(a)ε_(5, λb)   (12a)

n_(λa)=ω_(v)n_(λb)   (12b)

[0104] where

n_(λa≠ω) _(a)n_(λb)   (13a)

ε_(5, λa)≠ω_(v)ε_(5, λb)  (13b)

[0105] It is often the case that both equations (12) and (13) can besimultaneously satisfied. Multiplying equation (11) by ω_(a) andsubtracting the result from equation (9) yields a non-zero secondaryreference which is a linear sum of secondary signal components:$\begin{matrix}\begin{matrix}{{n^{\prime}(t)} = {{{S_{\lambda \quad a}(t)} - {\omega_{a}{S_{\lambda \quad b}(t)}}} = {{n_{\lambda \quad a}(t)} - {\omega_{a}{n_{\lambda \quad b}(t)}}}}} \\{= {{\Sigma_{i = 1}^{4}ɛ_{i,{\lambda \quad a}}c_{i}{x_{i}(t)}} + {\Sigma_{i = 6}^{N}\quad ɛ_{i,{\lambda \quad a}}c_{i}{x_{i}(t)}} -}}\end{matrix} & \left( {14a} \right) \\\begin{matrix}{{~~~~~~~~~~~~~~~}{{\Sigma_{i = 1}^{4}\quad \omega_{a}ɛ_{i,{\lambda \quad b}}c_{i}{x_{i}(t)}} + {\Sigma_{i = 6}^{N}\quad \omega_{a}ɛ_{i,{\lambda \quad b}}c_{i}{x_{i}(t)}}}} \\{= {{\Sigma_{i = 1}^{4}\quad c_{i}{{x_{i}(t)}\quad\left\lbrack {ɛ_{i,{\lambda \quad a}} - {\omega_{a}ɛ_{i,{\lambda \quad b}}}} \right\rbrack}} +}}\end{matrix} & \left( {15a} \right) \\{{~~~~~~~~~~~~~~~}{\Sigma_{i = 6}^{N}\quad c_{i}{{x_{i}(t)}\quad\left\lbrack {ɛ_{i,{\lambda \quad a}} - {\omega_{a}ɛ_{i,{\lambda \quad b}}}} \right\rbrack}}} & \left( {16a} \right)\end{matrix}$

[0106] Multiplying equation (11) by ω_(v) and subtracting the resultfrom equation (9) yields a primary reference which is a linear sum ofprimary signal components: $\begin{matrix}{{s^{\prime}(t)} = {{{S_{\lambda \quad a}(t)} - {\omega_{V}\quad {S_{\lambda \quad b}(t)}}} = {{s_{\lambda \quad a}(t)} - {\omega_{V}\quad {s_{\lambda \quad b}(t)}}}}} & \left( {14b} \right) \\{\quad {= {{c_{5}{x_{5}(t)}ɛ_{5,{\lambda \quad a}}} - {\omega_{V}\quad c_{5}{x_{5}(t)}ɛ_{5,{\lambda \quad b}}}}}} & \left( {15b} \right) \\{\quad {= {c_{5}{{{x_{5}(t)}\quad\left\lbrack {ɛ_{5,{\lambda \quad a}} - {\omega_{V}\quad ɛ_{5,{\lambda \quad b}}}} \right\rbrack}.}}}} & \left( {16b} \right)\end{matrix}$

[0107] A sample of either the secondary reference n′(t) or the primaryreference s′(t), and a sample of either measured signal S_(λa)(t) orS_(λb)(t), are input to a correlation canceler 27, such as an adaptivenoise canceler 30, an example of which is shown in FIGS. 5a and 5 b anda preferred example of which is discussed herein under the headingPREFERRED, CORRELATION CANCELER USING A JOINT PROCESS ESTIMATORIMPLEMENTATION. The correlation canceler 27 removes either the secondaryportion n_(λa)(t) or n_(λb)(t), or the primary portions, s_(λa)(t) ors_(λb)(t), of the measured signal yielding a good approximation toeither the primary signals s″_(λa)(t)≈s″_(λa)(t)/ε_(5,λa)x₅(t) ors″_(λb)(t)≈ε_(5,λb)c₅x₅(t) or the secondary signals n″_(λa)(t)≈n_(λa)(t)or n″_(λb)(t)≈n_(λb)(t). In the event that the primary signals areobtained, the concentration c₅(t) may then be determined from theapproximation to the primary signal s″_(λa)(t) or s″_(λb)(t) accordingto:

c₅(t)≈s″_(λa)(t)/ε_(5,λa)x₅(t)≈s″_(λb)(t)/ε_(5,λb)x₅(t).   (17)

[0108] As discussed previously, the absorption coefficients are constantat each wavelength λa and λb and the thickness of the primary signalcomponent, x₅(t) in this example, is often known or can be determined asa function of time, thereby allowing calculation of the concentrationc₅(t) of constituent A₅.

Determination of Concentration or Saturation in a Volume Containing Morethan One Constituent

[0109] Referring to FIG. 6b, another material having N differentconstituents arranged in layers is shown. In this material, twoconstituents A₅ and A₆ are found within one layer having thicknessx_(5,6)(t)=x₅(t)+x₆(t), located generally randomly within the layer.This is analogous to combining the layers of constituents A₅ and A₆ inFIG. 6a. A combination of layers, such as the combination of layers ofconstituents A₅ and A₆, is feasible when the two layers are under thesame total forces which result in the same change of thee optical pathlengths x₅(t) and x₆(t) of the layers.

[0110] Often it is desirable to find the concentration or thesaturation, i.e., a percent concentration, of one constituent within agiven thickness which contains more than one constituent and is subjectto unique forces. A determination of the concentration or the saturationof a constituent within a given volume may be made with any number ofconstituents in the volume subject to the same total forces andtherefore under the same perturbation or change. To determine thesaturation of one constituent in a volume comprising many constituents,as many measured signals as there are constituents which absorb incidentlight energy are necessary. It will be understood that constituentswhich do not absorb light energy are not consequential in thedetermination of saturation. To determine the concentration, as manysignals as there are constituents which absorb incident light energy arenecessary as well as information about the sum of concentrations.

[0111] It is often the case that a thickness under unique motioncontains only two constituents. For example, it may be desirable to knowthe concentration or saturation of A₅ within a given volume whichcontains A₅ and A₆. In this case, the primary signals s_(λa)(t) ands_(λb)(t) comprise terms related to both A₅ and A₆ so that adetermination of the concentration or saturation of A₅ or A₆ in thevolume may be made. A determination of saturation is discussed herein.It will be understood that the concentration of A₅ in a volumecontaining both A₅ and A₆ could also be determined if it is known thatA₅+A₆=1, i.e., that there are no constituents in the volume which do notabsorb incident light energy at the particular measurement wavelengthschosen. The measured signals S_(λa)(t) and S_(λb)(t) can be written(logarithm converted) as: $\begin{matrix}{{S_{\lambda \quad a}(t)} = {{ɛ_{5,\lambda_{a}}c_{5}{x_{5,6}(t)}} + {ɛ_{6,{\lambda \quad a}}c_{6}{x_{5,6}(t)}} + {n_{\lambda \quad a}(t)}}} & \left( {18a} \right) \\{\quad {= {{s_{\lambda \quad a}(t)} + {n_{\lambda \quad a}(t)}}}} & \left( {18b} \right) \\{{S_{\lambda \quad b}(t)} = {{ɛ_{5,{\lambda \quad b}}c_{5}{x_{5,6}(t)}} + {ɛ_{6,{\lambda \quad b}}c_{6}{x_{5,6}(t)}} + {n_{\lambda \quad b}(t)}}} & \left( {19a} \right) \\{\quad {= {{s_{\lambda \quad b}(t)} + {{n_{\lambda \quad b}(t)}.}}}} & \left( {19b} \right)\end{matrix}$

[0112] It is also often the case that there may be two or morethicknesses within a medium each containing the same two constituentsbut each experiencing a separate motion as in FIG. 6c. For example, itmay be desirable to know the concentration or saturation of A₅ within agiven volume which contains A₅ and A₆ as well as the concentration orsaturation of A₃ within a given volume which contains A₃ and A₄, A₃ andA₄ having the same constituency as A₅ and A₆, respectively. In thiscase, the primary signals s_(λa)(t) and s_(λb)(t) again comprise termsrelated to both A₅ and A₆ and portions of the secondary signalsn_(λa)(t) and n_(λb)(t) comprise terms related to both A₃ and A₄. Thelayers, A₃ and A₄, do not enter into the primary equation because theyare assumed to be perturbed by random or erratic secondary forces whichare uncorrelated with the primary force. Since constituents 3 and 5 aswell as constituents 4 and 6 are taken to be the same, they have thesame absorption coefficients. i.e. ε_(3,λa)=ε_(5,λa), ε_(3,λb)=ε_(5,λb),ε_(4,λa)=ε_(6,λa) and ε_(4,λb)=ε_(6,λb). Generally speaking, however, A₃and A₄ will have different concentrations than A₅ and A₆ and willtherefore have a different saturation. Consequently a single constituentwithin a medium may have one or more saturations associated with it. Theprimary and secondary signals according to this model may be written as:

s _(λa)(t)=[ε_(5, λa) c ₅+ε_(6, λa) c ₆ ]x _(5, 6)(t)   (20a)

n _(λa)(t)=[ε_(5, λa) c ₃+ε_(6, λa) c ₄ ]x _(3, 4)(t)+Σ² _(i=1)ε_(i,λa)c ₁ x _(i)(t)+Σ^(N) _(i=7)ε_(i,λa) c _(i) x _(i)(t)   (20b)

n _(λa)(t)=[ε_(5, λa) c ₃+ε_(6, λa) c ₄ ]x _(3, 4)(t)+n _(λa)(t)   (20c)

s _(λb)(t)=[ε_(5, λb) c ₅+ε_(6, λb) c ₆ ]x _(5, 6)(t)   (21a)

n _(λb)(t)=[ε_(5, λb) c ₃+ε_(6, λb) c ₄ ]x _(3, 4)(t)+Σ² _(i=1)ε_(i,λb)c _(i) x _(i)(t)+Σ^(N) _(i=7)ε_(i, λb) c _(i) x _(i)(t).   (21b)

n _(λb)(t)=[ε_(5, λb) c ₃+ε_(6, λb) c ₄ ]x _(3, 4)(t)+n _(λb)(t)   (21c)

[0113] where signals n_(λa)(t) and n_(λb)(t) are similar to thesecondary signals n_(λa)(t) and n_(λb)(t) except for the omission of the3, 4 layer.

[0114] Any signal portions whether primary or secondary, outside of aknown bandwidth of interest, including the constant undesired secondarysignal portion resulting from the generally constant absorption of theconstituents when not under perturbation, should be removed to determinean approximation to either the primary signal or the secondary signalwithin the bandwidth of interest. This is easily accomplished bytraditional band pass filtering techniques. As in the previous example,it is often the case that the total perturbation or change affecting thelayers associated with the secondary signal components is caused byrandom or erratic forces, causing the thickness of each layer, or theoptical path length of each layer, x_(i)(t), to change erratically,producing a random or erratic secondary signal component n_(λa)(t).Regardless of whether or not the secondary signal portion n_(λa)(t) iserratic, the secondary signal component n_(λa)(t) can be removed orderived via a correlation canceler, such as an adaptive noise canceler,having as one input a secondary reference n′(t) or a primary references′(t) determined by a processor of the present invention as long as theperturbation in layers other than the layer of constituents A₅ and A₆ isdifferent than the perturbation in the layer of constituents A₅ and A₆.Either the erratic secondary signal components n_(λa)(t) and n_(λb)(t)or the primary components s_(λa)(t) and s_(λb)(t) may advantageously beremoved from equations (18) and (19), or alternatively equations (20)and (21), by a correlation canceler. The correlation canceler, again,requires a sample of either the primary reference s′(t) or the secondaryreference n′(t) and a sample of either of the composite signalsS_(λa)(t) or S_(λb)(t) of equations (18) and (19).

Determination of Primary and Secondary Reference Signals for SaturationMeasurements

[0115] Two methods which may be used by a processor of the presentinvention to determine either the secondary reference n′(t) or theprimary reference s′(t) are a ratiometric method and a constantsaturation method. One embodiment of a physiological monitorincorporating a processor of the present invention utilizes theratiometric method wherein the two wavelengths λa and λb, at which thesignals S_(λa)(t) and S_(λb)(t) are measured, are specifically chosensuch that a relationship between the absorption coefficients ε_(5,λa),ε_(5,λb), ε_(6,λa) and ε_(6,λb) exists, i.e.

ε_(5,λa)/ε_(6,λa)=ε_(5,λb)/ε_(6,λb)   (22)

[0116] The measured signals S_(λa)(t) and S_(λb)(t) can be factored andwritten as:

S _(λa)(t)=ε_(6, λa)[(ε_(5,λa)/ε_(6, λa))c ₅ x _(5,6)(t)+c ₆ x_(5,6)(t)]+n _(λa)(t)   (23a)

S _(λa)(t)=ε_(6, λa)[(ε_(5,λa)/ε_(6, λa))c ₅ x _(5,6)(t)+c ₆ x_(5, 6)(t)+(ε_(5,λa)/ε_(6,λa))c ₃ x _(3,4)(t)+c ₄ x _(3,4)(t)]+n_(λa)(t)   (23b)

S _(λa)(t)=s _(λa) +n _(λa)(t)   (23c)

S _(λb)(t)=ε_(6,λb)[(ε_(5,λb)/ε_(6, λb))c ₅ x _(5,6)(t)+c ₆ x_(5,6)(t)]+n _(λb)(t)   (24a)

S _(λb)(t)=ε_(6, λb)[(ε_(5,λb)/ε_(6,λb))c ₅ x _(5,6)(t)+c ₆ x_(5,6)(t)+(ε_(5, λb)/ε_(6,λb))c ₃ x _(3,4)(t)+c ₄ x _(3,4)(t)]+n_(λb)(t)   (24b)

S _(λb)(t)=s _(λb) +n _(λb)(t).   (24c)

[0117] The wavelengths λa and λb, chosen to satisfy equation (22) causethe terms within the square brackets to be equal, thereby causing theterms other than n_(λa)(t) and n_(λb)(t) to be linearly dependent. Then,proportionality constants ω_(av) and ω_(e) may be found for thedetermination of a non-zero primary and secondary reference

ε_(6, λa)=ω_(av)ε_(6, λb)   (25a)

n _(λa)(t)=ω_(e) n _(λb)(t)   (25b)

ε_(6, λa)≈ω_(e)ε_(6, λb)   (26a)

n_(λa)(t)≈ω_(av)n_(λb)(t)   (26b)

[0118] It is often the case that both equations (25) and (26) can besimultaneously satisfied. Additionally, since the absorptioncoefficients of each constituent are constant with respect towavelength, the proportionality constants ω_(av) and ω_(e) can be easilydetermined. Furthermore, absorption coefficients of other constituentsA₁ through A₂ and A₇ through A_(N) are generally unequal to theabsorption coefficients of A₃, A₄, A₅ and A₆. Thus, the secondarycomponents n_(λa) and n_(λb) are generally not made linearly dependentby the relationships of equations (22) and (25).

[0119] Multiplying equation (24) by λ_(av) and subtracting the resultingequation from equation (23), a non-zero secondary reference isdetermined by: $\begin{matrix}\begin{matrix}{{n(t)} = {{S_{\lambda \quad a}(t)} - {\omega_{av}{S_{\lambda \quad b}(t)}}}} \\{= {{n_{\lambda \quad a}(t)} - {\omega_{av}{{n_{\lambda \quad b}(t)}.}}}}\end{matrix} & \left( {27a} \right)\end{matrix}$

[0120] Multiplying equation (24) by ω_(e) and subtracting the resultingequation from equation (23), a non-zero primary reference is determinedby: $\begin{matrix}\begin{matrix}{{s(t)} = {{S_{\lambda \quad a}(t)} - {\omega_{e}{S_{\lambda \quad b}(t)}}}} \\{= {{s_{\lambda \quad a}(t)} - {\omega_{e}{{s_{\lambda \quad b}(t)}.}}}}\end{matrix} & \left( {27b} \right)\end{matrix}$

[0121] An alternative method for determining reference signals from themeasured signals S_(λa)(t) and S_(λb)(t) using a processor of thepresent invention is the constant saturation approach. In this approach,it is assumed that the saturation of A₅ in the volume containing A₅ andA₆ and the saturation of A₃ in the volume containing A₃ and A₄ remainsrelatively constant over some period of time, i.e.:

Saturation(A ₅(t))=c ₅(t)/[c ₅(t)+c ₆(t)]  (28a)

Saturation(A ₃(t))=c ₃(t)/[c ₃(t)+c ₄(t)]  (28b)

Saturation(A ₅(t))={1+[c ₆(t)/c ₅(t)]}⁻¹   (29a)

Saturation(A ₃(t))={1+[c ₄(t)/c ₃(t)]}⁻¹   (29b)

[0122] are substantially constant over many samples of the measuredsignals S_(λa) and S_(λb). This assumption is accurate over many samplessince saturation generally changes relatively slowly in physiologicalsystems.

[0123] The constant saturation assumption is equivalent to assumingthat:

c ₅(t)/c ₆(t)=constant₁   (30a)

c ₃(t)/c ₄(t)=constant₂   (30b)

[0124] since the only other term in equations (29a) and (29b) is aconstant, namely the numeral 1.

[0125] Using this assumption, the proportionality constants ω_(a) andω_(v) which allow determination of the secondary reference signal n′(t)and the primary reference signal s′(t) in the constant saturation methodare: $\begin{matrix}{\omega_{a} = \frac{{ɛ_{5,{\lambda \quad a}}c_{5}{x_{5,6}(t)}} + {ɛ_{6,{\lambda \quad a}}c_{6}{x_{5,6}(t)}}}{{ɛ_{5,{\lambda \quad b}}c_{5}{x_{5,6}(t)}} + {ɛ_{6,{\lambda \quad b}}c_{6}{x_{5,6}(t)}}}} & \left( {31a} \right) \\{\quad {= {{s_{\lambda \quad a}(t)}/{s_{\lambda \quad b}(t)}}}} & \left( {32a} \right) \\{\quad {= \frac{{ɛ_{5,{\lambda \quad a}}c_{5}} + {ɛ_{6,{\lambda \quad a}}c_{6}}}{{ɛ_{5,{\lambda \quad b}}c_{5}} + {ɛ_{6,{\lambda \quad b}}c_{6}}}}} & \left( {33a} \right) \\{\quad {= \frac{{ɛ_{5,{\lambda \quad a}}\left( {c_{5}/c_{6}} \right)} + ɛ_{6,{\lambda \quad a}}}{{ɛ_{5,{\lambda \quad b}}\left( {c_{5}/c_{6}} \right)} + ɛ_{6,{\lambda \quad b}}}}} & \left( {34a} \right) \\{\quad {{{\approx {{s_{\lambda \quad a}^{''}(t)}/{s_{\lambda \quad b}^{''}(t)}}} = {constant}_{3}};{where}}} & \left( {35a} \right) \\{{~~~~~~~~}{{{n_{\lambda \quad a}(t)} \neq {{\omega_{a}(t)}n_{\lambda \quad b}(t)}}{and}}} & \left( {36a} \right) \\{\omega_{V} = \frac{{ɛ_{5,{\lambda \quad a}}c_{3}{x_{3,4}(t)}} + {ɛ_{6,{\lambda \quad a}}c_{4}{x_{3,4}(t)}}}{{ɛ_{5,{\lambda \quad b}}c_{3}{x_{3,4}(t)}} + {ɛ_{6,{\lambda \quad b}}c_{4}{x_{3,4}(t)}}}} & \left( {31b} \right) \\{\quad {= {{n_{\lambda \quad a}(t)}/{n_{\lambda \quad b}(t)}}}} & \left( {32b} \right) \\{\quad {= \frac{{ɛ_{5,{\lambda \quad a}}c_{3}} + {ɛ_{6,{\lambda \quad a}}c_{4}}}{{ɛ_{5,{\lambda \quad b}}c_{3}} + {ɛ_{6,{\lambda \quad b}}c_{4}}}}} & \left( {33b} \right) \\{\quad {= \frac{{ɛ_{5,{\lambda \quad a}}\left( {c_{3}/c_{4}} \right)} + ɛ_{6,{\lambda \quad a}}}{{ɛ_{5,{\lambda \quad b}}\left( {c_{3}/c_{4}} \right)} + ɛ_{6,{\lambda \quad b}}}}} & \left( {34b} \right) \\{\quad {{{\approx {{n_{\lambda \quad a}^{''}(t)}/{n_{\lambda \quad b}^{''}(t)}}} = {constant}_{4}};{where}}} & \left( {35b} \right) \\{{~~~~~~~~~~~~~}{{s_{\lambda \quad a}(t)} \neq {{\omega_{V}(t)}\quad {{s_{\lambda \quad b}(t)}.}}}} & \left( {36b} \right)\end{matrix}$

[0126] It is often the case that both equations (32) and (36) can besimultaneously satisfied to determine the proportionality constantsω_(a) and ω_(v). Additionally, the absorption coefficients at eachwavelength ε_(5,λa), ε_(6,λa), ε_(5,λb), and ε_(6,λb) are constant andthe central assumption of the constant saturation method is thatc₅(t)/c₆(t) and c₃(t)/c₄(t) are constant over many sample periods. Thus,new proportionality constants ω_(a) and ω_(v) may be determined everyfew samples from new approximations to either the primary or secondarysignal as output from the correlation canceler. Thus, the approximationsto either the primary signals s_(λa)(t) and s_(λb)(t) or the secondarysignals n_(λa)(t) and n_(λb)(t), found by the correlation canceler for asubstantially immediately preceding set of samples of the measuredsignals S_(λa)(t) and S_(λb)(t) are used in a processor of the presentinvention for calculating the proportionality constants, ω_(a) andω_(v), for the next set of samples of the measured signals S_(λa)(t) andS_(λb)(t).

[0127] Multiplying equation (19) by ω_(a) and subtracting the resultingequation from equation (18) yields a non-zero secondary referencesignal:

n′(t)=S _(λa)(t)−ω_(a) S _(λb)(t)=n _(λa)(t)−ω_(a) n _(λb)(t).   (37a)

[0128] Multiplying equation (19) by ω_(v) and subtracting the resultingequation from equation (18) yields a non-zero primary reference signal:

S′(t)=S _(λa)(t)−ω_(v) S _(λb)(t)=s _(λa)(t)−ω_(v) s _(λb)(t).   (37b)

[0129] When using the constant saturation method, it is not necessaryfor the patient to remain motionless for a short period of time suchthat an accurate initial saturation value can be determined by knownmethods other than correlation canceling. With no erratic,motion-induced signal portions, a physiological monitor can very quicklyproduce an initial value of the saturation of A₅ in the volumecontaining A₅ and A₆. An example of a saturation calculation is given inthe article “SPECTROPHOTOMETRIC DETERMINATION OF OXYGEN SATURATION OFBLOOD INDEPENDENT OF THE PRESENT OF INDOCYANINE GREEN” by G. A. Mook, etal., wherein determination of oxygen saturation in arterial blood isdiscussed. Another article discussing the calculation of oxygensaturation is “PULSE OXIMETRY: PHYSICAL PRINCIPLES, TECHNICALREALIZATION AND PRESENT LIMITATIONS” by Michael R. Neuman. Then, withvalues for the coefficients ω_(a) and ω_(v) determined, a correlationcanceler may be utilized with a secondary reference n′(t) or a primaryreference s′(t) determined by the constant saturation method.

Determination of Signal Coefficients for Primary and Secondary ReferenceSignals Using the Constant Saturation Method

[0130] The reference processor 26 of FIG. 4a and FIG. 4b of the presentinvention may be configured to multiply the second measured signalS_(λb)(t)=s_(λb)(t)+n_(λb)(t) by a plurality of signal coefficients ω₁,ω₂, . . . ω_(n) and then subtract each result from the first measuredsignal S_(λa)(t)=s_(λa)(t)+n_(λa)(t) to obtain a plurality of referencesignals

r′(ω, t)=s _(λa)(t)−ωs _(λb)(t)+n _(λa)(t)−ωn_(λ) _(b)(t)   (38)

[0131] for ω=ω₁, ω₂, . . . ω_(n) as shown in FIG. 7a.

[0132] In order to determine either the primary reference s′(t) or thesecondary reference n′(t) from the above plurality of reference signalsof equation (38), signal coefficients ω_(a) and ω_(v) must be determinedfrom the plurality of signal coefficients ω₁, ω₂, . . . ω_(n). Thecoefficients ω_(a) and ω_(v) are such that they cause either the primarysignal portions s_(λa)(t) and s_(λb)(t) or the secondary signal portionsn_(λa)(t) and n_(λb)(t) to cancel or nearly cancel when they aresubstituted into the reference function r′(ω, t), e.g.

s _(λa)(t)=ω_(a) s _(λb)(t)   (39a)

n _(λa)(t)=ω_(v) n _(λb)(t)   (39b)

n′(t)=r′(ω_(a) , t)=n _(λa)(t)−ω_(a) n _(λb)(t)   (39c)

s′(t)=r′(ω_(v) , t)=s _(λa)(t)−ω_(v) s _(λb)(t).   (39d)

[0133] In practice, one does not usually have significant priorinformation about either the primary signal portions s_(λa)(t) ands_(λb)(t) or the secondary signal portions n_(λa)(t) and n_(λb)(t) ofthe measured signals S_(λa)(t) and S_(λb)(t). The lack of thisinformation makes it difficult to determine which of the plurality ofcoefficients ω₁, ω₂, . . . ω_(n) correspond to the signal coefficientsω_(a)s_(λa)(t)/s_(λb)(t) and ω_(v)=n_(λa)(t)/n_(λb)(t). Herein thepreferred approach to determine the signal coefficients ω_(a) and ω_(v)from the plurality of coefficients ω₁, ω₂, . . . ω_(n) employs the useof a correlation canceler 27, such as an adaptive noise canceler, whichtakes a first input which corresponds to one of the measured signalsS_(λa)(t) or S_(λb)(t) and takes a second input which corresponds tosuccessively each one of the plurality of reference signals r′(ω₁, t),r′(ω₂, t), . . . , r′(ω_(n), t) as shown in FIG. 7a. For each of thereference signals r′(ω₁, t), r′(ω₂, t), . . . , r′(ω_(n), t) thecorresponding output of the correlation canceler 27 is input to anintegrator 29 for forming a cumulative output signal. The cumulativeoutput signal is subsequently input to an extremum detector 31. Thepurpose of the extremum detector 31 is to chose signal coefficientsω_(a) and ω_(v) from the set ω₁, ω₂, . . . ω_(n) by observing whichprovide a maximum in the cumulative output signal as in FIGS. 7b and 7c. In other words, coefficients Which provide a maximum integratedoutput, such as energy or power, from the correlation canceler 27correspond to the signal coefficients ω_(a), and ω_(v). One could alsoconfigure a system geometry which would require one to locate thecoefficients from the set ω₁, ω₂, . . . ω_(n) which provide a minimum orinflection in the cumulative output signal to identify the signalcoefficients ω_(a) and ω_(v)

[0134] Use of a plurality of coefficients in the processor of thepresent invention in conjunction with a correlation canceler 27 todetermine the signal coefficients ω_(a) and ω_(v) may be demonstrated byusing the properties of correlation cancellation. If x, y and z aretaken to be any collection of three time varying signals, then theproperties of a generic correlation canceler C(x, y) may be defined asfollows:

Property (1) C(x, y)=0 for x, y correlated

Property (2) C(x, y)=x for x, y uncorrelated

Property (3) C(x+y, z)=C(x, z)+C(y, z).   (40)

[0135] With properties (1), (2) and (3) it is easy to demonstrate thatthe energy or power output of a correlation canceler with a first inputwhich corresponds to one of the measured signals S_(λa)(t) or S_(λb)(t)and a second input which corresponds to successively each one of aplurality of reference signals r′(ω₁, t), r′(ω₂, t), . . . , r′(ω_(n),t) can determine the signal coefficients ω_(a) and ω_(v) needed toproduce the primary reference s′(t) and secondary reference n′(t). If wetake as a first input to the correlation canceler the measured signal.S_(λa)(t) and as a second input the plurality of reference signalsr′(ω₁, t), r′(ω₂, t), . . . , r′(ω_(n), t) then the outputs of thecorrelation canceler C(S_(λa)(t), r′(ω_(j),t)) for j=1, 2, . . . , n maybe written as

C(s_(λa)(t)+n_(λa)(t),s_(λa)(t)−ω_(j)s_(λb)(t)+n_(λa)(t)−ω_(j)n_(λb)(t))  (41)

[0136] where j=1, 2, . . . , n and we have used the expressions

r′(ω, t)=S _(λa)(t)−ωS_(λb)(t)   (42)

S _(λa)(t)=s _(λa)(t)+n _(λa)(t)   (43a)

S _(λb)(t)=s _(λb)(t)+n _(λb)(t).   (43b)

[0137] The use of property (3) allows one to expand equation (41) intotwo terms

C(S _(λa)(t),r′(ω,t)=C(s _(λa)(t),s _(λa)(t)−ωs _(λb)(t)+n _(λa)(t)−ωn_(λb)(t)+

+C(n _(λa)(t),s _(λa)(t)−ωs _(λb)(t)+n _(λa)(t)−ωn _(λb)(t))   (44)

[0138] so that upon use of properties (1) and (2) the correlationcanceler output is given by

C(S _(λa)(t), r′(ω_(j) ,t)=s_(λa)(t)δ(ω_(j−ω) _(a))+n_(λa)(t)δ(ω_(j)−ω_(v))   (45)

[0139] where δ(x) is the unit impulse function

δ(x)=0 if x≠0

δ(x)=1 if x=0.   (46)

[0140] The time variable, t, of the correlation canceler outputC(S_(λa)(t), r′(ω_(j), t)) may be eliminated by computing its energy orpower. The energy of the correlation canceler output is given by$\begin{matrix}\begin{matrix}{{E_{\lambda \quad a}\left( \omega_{j} \right)} = {\int{c^{2}\left( {{s_{\lambda \quad a}(t)},{{r^{\prime}\left( {\omega_{j},t} \right)}{t}}} \right.}}} \\{= {{{\delta \left( {\omega - \omega_{a}} \right)}{\int{{s_{\lambda \quad a}^{2}(t)}{t}}}} + {{\delta \left( {\omega - \omega_{V}} \right)}{\int{{n_{\lambda \quad a}^{2}(t)}{{t}.}}}}}}\end{matrix} & \left( {47a} \right)\end{matrix}$

[0141] It must be understood that one could, equally well, have chosenthe measured signal S_(λb)(t) as the first input to the correlationcanceler and the plurality of reference signals r′(ω₁, t), r′(ω₂, t), .. . , r′(ω_(n), t) as the second input. In this event, the correlationcanceler energy output is $\begin{matrix}\begin{matrix}{{E_{\lambda \quad b}(\omega)} = {\int{c^{2}\left( {{s_{\lambda \quad b}(t)},{{r^{\prime}\left( {\omega,t} \right)}{t}}} \right.}}} \\{= {{{\delta \left( {\omega - \omega_{a}} \right)}{\int{{s_{\lambda \quad b}^{2}(t)}{t}}}} + {{\delta \left( {\omega - \omega_{V}} \right)}{\int{{n_{\lambda \quad b}^{2}(t)}{{t}.}}}}}}\end{matrix} & \left( {47b} \right)\end{matrix}$

[0142] It must also be understood that in practical situations the useof discrete time measurement signals may be employed as well ascontinuous time measurement signals. In the event that discrete timemeasurement signals are used integration approximation methods such asthe trapezoid rule, midpoint rule, Tick's rule, Simpson's approximationor other techniques may be used to compute the correlation cancelerenergy or power output. In the discrete time measurement signal case,the energy output of the correlation canceler may be written, using thetrapezoid rule, as

E _(λa)(ω)=δ(ω−ω_(a))Δt{Σ ^(n) _(i=0) s ² _(λa)(t _(i))−0.5(s ² _(λa)(t_(O))+s ² _(λa)(t _(n)))}+δ(ω−ω_(v))Δt{Σ ^(n) _(i=0) ^(n) ² _(λa)(t_(i))−0.5(n ² _(λa)(t _(O))+n ² _(λa)(t _(n)))}  (48a)

E _(λb)(ω)=δ(ω−ω_(a))Δt{Σ ^(n) _(i=0) s ² _(λb)(t _(i))−0.5(s ² _(λb)(t_(O))+s ² _(λb)(t _(n)))}+δ(ω−ω_(v))Δt{Σ ^(n) _(i=0) n ² _(λb)(t_(i))−0.5(n ² _(λb)(t _(O))+n ² _(λb)(t _(n)))}  (48b)

[0143] where t_(i) is the i^(th) discrete time, t_(O) is the initialtime, t_(n) is the final time and Δt is the time between discrete timemeasurement samples.

[0144] The energy functions given above, and shown in FIG. 7b, indicatethat the correlation canceler output is usually zero due to correlationbetween the measured signal S_(λa)(t) or S_(λb)(t) and many of theplurality of reference signals r′(ω₁, t), r′(ω₂, t), . . . , r′(ω_(n),t)r′(ω, t). However, the energy functions are non zero at values ofω_(j) which correspond to cancellation of either the primary signalportions s_(λa)(t) and s_(λb)(t) or the secondary signal portionsn_(λa)(t) and n_(λb)(t) in the reference signal r′(ω_(j), t). Thesevalues correspond to the signal coefficients ω_(a) and ω_(v).

[0145] It must be understood that there may be instances in time wheneither the primary signal portions s_(λa)(t) and s_(λb)(t) or thesecondary signal portions n_(λa)(t) and n_(λb)(t) are identically zeroor nearly zero. In these cases, only one signal coefficient value willprovide maximum energy or power output of the correlation canceler.

[0146] Since there may be more than one signal coefficient value whichprovides maximum correlation canceler energy or power output, anambiguity may arise. It may not be immediately obvious which signalcoefficient together with the reference function r′(ω, t) provideseither the primary or secondary reference. In such cases, it isnecessary to consider the constraints of the physical system at hand.For example, in pulse oximetry, it is known that arterial blood, whosesignature is the primary plethysmographic wave, has greater oxygensaturation than venous blood, whose signature is the secondary erraticor random signal. Consequently, in pulse oximetry, the ratio of theprimary signals due to arterial pulsation ω_(a)=s_(λa)(t)/s_(λb)(t) isthe smaller of the two signal coefficient values while the ratio of thesecondary signals due to mainly venous blood dynamicsλ_(v)=n_(λa)(t)/n_(λb)(t) is the larger of the two signal coefficientvalue, assuming λa=660 nm and λb=940 nm.

[0147] It must be understood that in practical implementations of theplurality of reference signals and cross correlator technique, the idealfeatures listed as properties (1), (2) and (3) above will not beprecisely satisfied but will be approximations thereof. Therefore, inpractical implementations of the present invention, the correlationcanceler energy curves depicted in FIG. 7b will not consist ofinfinitely narrow delta functions but will have finite width associatedwith them as depicted in FIG. 7c.

[0148] It should also be understood that it is possible to have morethan two signal coefficient values which produce maximum energy or poweroutput from a correlation canceler. This situation will arise when themeasured signals each contain more than two components each of which arerelated by a ratio as follows:

S _(λa)(t)=Σ^(n) _(i=1) f _(λa, i)(t) S _(λb)(t)=Σ^(n) _(i=1) f_(λb, i)(t)   (49)

[0149] where

f _(λa, i)(t)=ω_(i) f _(λa, i)(t) i=1, . . . , n

ω_(i)≠ω_(j).

[0150] The ability to employ reference signal techniques together with acorrelation cancellation, such as an adaptive noise canceler, todecompose a signal into two or more signal components each of which isrelated by a ratio is a further aspect of the present invention.

Preferred Correlation Canceler Using a Joint Process EstimatorImplementation

[0151] Once either the secondary reference n′(t) or the primaryreference s′(t) is determined by the processor of the present inventionusing either the above described ratiometric or constant saturationmethods, the correlation canceler can be implemented in either hardwareor software. The preferred implementation of a correlation canceler isthat of an adaptive noise canceler using a joint process estimator.

[0152] The least mean squares (LMS) implementation of the internalprocessor 32 described above in conjunction with the adaptive noisecanceler of FIG. 5a and FIG. 5b is relatively easy to implement, butlacks the speed of adaptation desirable for most physiologicalmonitoring applications of the present invention. Thus, a fasterapproach for adaptive noise canceling, called a least-squares latticejoint process estimator model, is preferably used. A joint processestimator 60 is shown diagrammatically in FIG. 8 and is described indetail in Chapter 9 of Adaptive Filter Theory by Simon Haykin, publishedby Prentice-Hall, copyright 1986. This entire book, including Chapter 9,is hereby incorporated herein by reference. The function of the jointprocess estimator is to remove either the secondary signal portionsn_(λa)(t) or n_(λb)(t) or the primary signal portions s_(λa)(t) ors_(λb)(t) from the measured signals s_(λa)(t) or s_(λb)(t), yieldingeither a signal s″_(λa)(t) or s″_(λb)(t) or a signal n″_(λa)(t) orn″_(λb)(t) which is a good approximation to either the primary signals_(λa)(t) or s_(λb)(t) or the secondary signal n_(λa)(t) or n_(λb)(t).Thus, the joint process estimator estimates either the value of theprimary signals s_(λa)(t) or s_(λb)(t) or the secondary signalsn_(λa)(t) or n_(λb)(t). The inputs to the joint process estimator 60 areeither the secondary reference n′(t) or the primary reference s′(t) andthe composite measured signal s_(λa)(t) or s_(λb)(t). The output is agood approximation to the signal s_(λa)(t) or s_(λb)(t) with either thesecondary signal or the primary signal removed, i.e. a goodapproximation to either s_(λa)(t), s_(λb)(t), n_(λa)(t) or n_(λb)(t).

[0153] The joint process estimator 60 of FIG. 8 utilizes, inconjunction, a least square lattice predictor 70 and a regression filter80. Either the secondary reference n′(t) or the primary reference s′(t)is input to the least square lattice predictor 70 while the measuredsignal s_(λa)(t) or s_(λb)(t) is input to the regression filter 80. Forsimplicity in the following description, s_(λa)(t) will be the measuredsignal from which either the primary portion s_(λa)(t) or the secondaryportion n_(λa)(t) will be estimated by the joint process estimator 60.However, it will be noted that S_(λb)(t) could equally well be input tothe regression filter 80 and the primary portion s_(λb)(t) or thesecondary portion n_(λb)(t) of this signal could equally well beestimated.

[0154] The joint process estimator 60 removes all. frequencies that arepresent in both the reference n′(t) or s′(t), and the measured signals_(λa)(t). The secondary signal portion n_(λa)(t) usually comprises.frequencies unrelated to those of the primary signal portion s_(λa)(t).It is highly improbable that the secondary signal portion n_(λa)(t)would be of exactly the same spectral content as the primary signalportion s_(λa)(t). However, in the unlikely event that the spectralcontent of s?,a(t) and n_(λa)(t) are similar, this approach will notyield accurate results. Functionally, the joint process estimator 60compares the reference input signal n′(t) or s′(t), which is correlatedto either the secondary signal portion n_(λa)(t) or the primary signalportion s_(λa)(t), and input signal s_(λa)(t) and removes allfrequencies which are identical. Thus, the joint process estimator 60acts as a dynamic multiple notch filter to remove those frequencies inthe secondary signal component n_(λa)(t) as they change erratically withthe motion of the patient or those frequencies in the primary signalcomponent s_(λa)(t) as they change with the arterial pulsation of thepatient. This yields a signal having substantially the same spectralcontent and amplitude as either the primary signal s_(λa)(t). or thesecondary signal n_(λa)(t). Thus, the output s″_(λa)(t) or n″_(λa)(t) ofthe joint process estimator 60 is a very good approximation to eitherthe primary signal s_(λa)(t) or the secondary signal n_(λa)(t) The jointprocess estimator 60 can be divided into stages, beginning with azero-stage and terminating in an m^(th)-stage, as.shown in FIG. 8. Eachstage, except for the zero-stage, is identical to every other stage. Thezero-stage is an input stage for the joint process estimator 60. Thefirst stage through the m^(th)-stage work on the signal produced in theimmediately previous stage, i.e., the (m−1)^(th)-stage, suchthat a goodapproximation to. either the primary signal s″_(λa)(t) or the secondarysignal n″_(λa)(t) is produced as output from the m^(th)-stage.

[0155] The least-squares lattice predictor 70 comprises registers 90 and92, summing elements 100 and 102, and delay elements 110. The registers90 and 92 contain multiplicative values of a forward reflectioncoefficient Γ_(f,m)(t) and a backward reflection coefficient rb,m(t)which multiply the reference signal n′(t) or s′(t) and signals derivedfrom the reference signal n′(t) or s′(t). Each stage of theleast-squares lattice predictor outputs a forward prediction errorf_(m)(t) and a backward prediction error b_(m)(t). The subscript m isindicative of the stage.

[0156] For each set of samples, i.e. one sample of the reference signaln′(t) or s′(t) derived substantially simultaneously with one sample ofthe measured signal s_(λa)(t), the sample of the reference signal n′(t)or s′(t) is input to the least-squares lattice predictor 70. Thezero-stage forward prediction error f₀(t) and the zero-stage backwardprediction error b₀(t) are set equal to the reference signal n′(t) ors′(t). The backward prediction error b₀(t) is delayed by one sampleperiod by the delay element 110 in the first stage of the least-squareslattice predictor 70. Thus, the immediately previous value of thereference n′(t) or s′(t) is used in calculations involving thefirst-stage delay element 110. The zero-stage forward prediction erroris added to the negative of the delayed zero-stage backward predictionerror b₀(t−1) multiplied by the forward reflection coefficient valueΓ_(f,1)(t) register 90 value, to produce a first-stage forwardprediction error f₁(t). Additionally, the zero-stage forward predictionerror f₀(t) is multiplied by the backward reflection coefficient valueΓ_(b,1)(t) register 92 value and added to the delayed zero-stagebackward prediction error b₀(t−1) to produce a first-stage backwardprediction error b₁(t). In each subsequent stage, m, of the least squarelattice predictor 70, the previous forward and backward prediction errorvalues, f_(m−1)(t) and b_(m−1)(t−1), the backward prediction error beingdelayed by one sample period, are used to produce values of the forwardand backward prediction errors for the present stage, f_(m)(t) andb_(m)(t).

[0157] The backward prediction error b_(m)(t) is fed to the concurrentstage, m, of the regression filter 80. There it is input to a register96, which contains a multiplicative regression coefficient valueκ_(m,λa)(t). For example, in the zero-stage of the regression filter 80,the zero-stage backward prediction error b₀(t) is multiplied by thezero-stage regression coefficient κ_(0,λa)(t) register 96 value andsubtracted from the measured value of the signal s_(λa)(t) at a summingelement 106 to produce a first stage estimation error signale_(1,λa)(t). The first-stage estimation error signal e_(1,λa)(t) is afirst approximation to either the primary signal or the secondarysignal. This first-stage estimation error signal e_(1,λa)(t) is input tothe first-stage of the regression filter 80. The first-stage backwardprediction error b₁(t), multiplied by the first-stage regressioncoefficient κ_(1,λa)(t) register 96 value is subtracted from thefirst-stage estimation error signal e_(1,λa)(t) to produce thesecond-stage estimation error e_(2,λa)(t). The second-stage estimationerror signal e_(2,λa)(t). is a second, somewhat better approximation toeither the primary signal s_(λa)(t) or the secondary signal n_(λa)(t).

[0158] The same processes are repeated in the least-squares latticepredictor 70 and the regression filter 80 for each stage until a goodapproximation e_(m,λa)(t), to either the primary signal s_(λa)(t) orthe. secondary signal n_(λa)(t) is determined. Each of the signalsdiscussed above, including the forward prediction error f_(m)(t), thebackward prediction error b_(m)(t), the estimation error signale_(m,λa)(t), is necessary to calculate the forward reflectioncoefficient Γ_(f,m)(t), the backward reflection coefficient Γ_(b,m)(t),and the regression coefficient κ_(m,λa)(t) register 90, 92, and 96values in each stage, m. In addition to the forward prediction errorf_(m)(t), the backward prediction error b_(m)(t), and the estimationerror e_(m,λa)(t) signals, a number of intermediate variables, not shownin FIG. 8 but based on the values labeled in FIG. 8, are required tocalculate the forward reflection coefficient Γ_(f,m)(t) the backwardreflection coefficient Γ_(b,m)(t), and the regression coefficientκ_(m,λa)(t) register 90, 92, and 96 values.

[0159] Intermediate variables include a weighted sum of the forwardprediction error squares J_(m)(t) a weighted sum of the backwardprediction error squares β_(m)(t), a scalar parameter Δ_(m)(t), aconversion factor γ_(m)(t), and another scalar parameter ρ_(m,λa)(t).The weighted sum of the forward prediction errors J_(m)(t) is definedas: $\begin{matrix}{{{_{m}(t)} = {\Sigma \quad \lambda_{i = 1}^{\overset{t}{t - i}}{{f_{m}(i)}}^{2}}};} & (50)\end{matrix}$

[0160] where λ without a wavelength identifier, a or b, is a constantmultiplicative value unrelated to wavelength and is typically less thanor equal to one, i.e., λ≦1. The weighted sum of the backward predictionerrors β_(m)(t) is defined as: $\begin{matrix}{{\beta_{m}(t)} = {\Sigma \quad \lambda_{i = 1}^{\overset{t}{t - i}}{{b_{m}(i)}}^{2}}} & (51)\end{matrix}$

[0161] where, again, λ without a wavelength identifier, a or b, is aconstant multiplicative value unrelated to wavelength and is typicallyless than or equal to one, i.e., λ≦1. These weighted sum intermediateerror signals can be manipulated such that they are more easily solvedfor, as described in Chapter 9, § 9.3. and defined hereinafter inequations (65) and (66).

Description of the Joint Process Estimator

[0162] The operation of the joint process estimator 60 is as follows.When the joint process estimator 60 is turned on, the initial values ofintermediate variables and signals including the parameter Δ_(m−1)(t),the weighted sum of the forward prediction error signals J_(m−1)(t), theweighted sum of the backward prediction error signals β_(m−1)(t), theparameter ρ_(m,λa)(t), and the zero-stage estimation error e_(0,λa)(t)are initialized, some to zero and some to a small positive number δ:

Δ_(m−1)(0)=0;   (52)

J _(m−1)(0)=6;   (53)

β_(m−1)(0)=6;   (54)

ρ_(m,λa)(0)=0;   (55)

e _(0,λa)(t)=s _(λa)(t) for t≧0.   (56)

[0163] After initialization, a simultaneous sample of the measuredsignal s_(λa)(t) or s_(λb)(t) and either the secondary reference n′(t)or the primary reference s′(t) are input to the joint process estimator60, as shown in FIG. 8. The forward and backward prediction errorsignals f₀(t) and b₀(t), and intermediate variables including theweighted sums of the forward and backward error signals J₀(t) and β₀(t),and the conversion factor γ₀(t) are calculated for the zero-stageaccording to:

f ₀(t)=b ₀(t)=n′(t)   (57a)

J ₀(t)=β₀(t)=λJ ₀(t−1)+|n′(t)|²   (58a)

γ₀(t−1)=1   (59a)

[0164] if a secondary reference n′(t) is used or according to:

f ₀(t)=b ₀(t)=s′(t)   (57b)

J ₀(t)=β₀(t)=λJ₀(t−1)+|s′(t)|²   (58b)

β₀(t−1)=1   (59b)

[0165] if a primary reference s′(t) is used where, again, λ without awavelength identifier, a or b, is a constant multiplicative valueunrelated to wavelength.

[0166] Forward reflection coefficient Γ_(f,m)(t), backward reflectioncoefficient Γ_(b,m)(t), and regression coefficient κ_(m,λa)(t) register90, 92 and 96 values in each stage thereafter are set according to theoutput of the previous stage. The forward reflection coefficientΓ_(f,1)(t), backward reflection coefficient Γ_(b,1)(t), and regressioncoefficient κ_(1,λa)(t) register 90, 92 and 96 values in the first stageare thus set according to algorithm using values in the zero-stage ofthe joint process estimator 60. In each stage, m≧1, intermediate valuesand register values including the parameter Δ_(m−1)(t); the forwardreflection coefficient Γ_(f,m)(t) register 90 value; the backwardreflection coefficient Γ_(b,m)(t) register 92 value; the forward andbackward error signals f_(m)(t) and b_(m)(t); the weighted sum ofsquared forward prediction errors J_(f,m)(t), as manipulated in § 9.3 ofthe Haykin book; the weighted sum of squared backward prediction errorsβ_(b,m)(t), as manipulated in § 9.3 of the Haykin book; the conversionfactor γ_(m)(t); the parameter ρ_(m,λa)(t); the regression coefficientκ_(m,λa)(t) register 96 value; and the estimation error e_(m+1λa)(t)value are set according to:

Δ_(m−1)(t)=λΔ_(m−1)(t−1 )+{b _(m−1)(t−1) f*_(m−1)(t)/γ_(m−1)(t−1)}  (60)

Γ_(f,m)(t)=−{Δ_(m−1)(t)/β_(m−1)(t−1)}(61)

Γ_(b,m)(t)=−{Δ*_(m−1)(t)/J_(m−1)(t)}  (62)

f _(m)(t)=f _(m−1)(t)+Γ*_(f,m)(t)b _(m−1)(t−1)   (63)

b _(m)(t)=b _(m−1)(t−1)+Γ*_(b,m)(t)f _(m−1)(t)   (64)

J _(m)(t)=J _(m−1)(t)−{|Δ_(m−1)(t)|²/β_(m−1)(t−1)}  (65)

β_(m)(t)=β_(m−1)(t−1)−{|Δ_(m−1)(t)|² /J _(m−1)(t)}  (66).

γ_(m)(t−1)=γ_(m−1)(t−1)−{|b _(m−1)(t−1)|²/β_(m−1)(t−1)}  (67)

ρ_(m,λa)(t)=λρ_(m,λa)(t−1)+{b _(m)(t)e* _(m,λa)(t)/γ_(m)(t)}  (68)

κ_(m,λa)(t)={ρ_(m,λa)(t)/β_(m)(t)}  (69)

e _(m+1,λa)(t)=e _(m,λa)(t)−κ*_(m)(t)b _(m)(t)   (70)

[0167] where a (*) denotes a complex conjugate.

[0168] These equations cause the error signals f_(m)(t), b_(m)(t),e_(m,λa)(t) to be squared or to be multiplied by one another, in effectsquaring the errors, and creating new intermediate error values, such asΔm₁(t). The error signals and the intermediate error values arerecursively tied together, as shown in the above equations (60) through(70). They interact to minimize the error signals in the next stage.

[0169] After a good approximation to either the primary signal s_(λa)(t)or the secondary signal n_(λa)(t) has been determined by the jointprocess estimator 60, a next set of samples, including a sample of themeasured signal s_(λa)(t) and a sample of either the secondary referencen′(t) or the primary reference s′(t), are input to the joint processestimator 60. The re-initialization process does not re-occur, such thatthe forward and backward reflection coefficient Γ_(f,m)(t) andΓ_(b,m)(t) register 90, 92 values and the regression coefficientκ_(m,λa)(t) register 96 value reflect the multiplicative values requiredto estimate either the primary signal portion s_(λa)(t) or the secondarysignal portion n_(λa)(t) of the sample of s_(λa)(t) input previously.Thus, information from previous samples is used to estimate either theprimary or secondary signal portion of a present set of samples in eachstage.

Flowchart of Joint Process Estimator

[0170] In a signal processor, such as a physiological monitor,incorporating a reference processor of the present invention todetermine a reference n′(t) or s′(t) for input to a correlationcanceler, a joint process estimator 60 type adaptive noise canceler isgenerally implemented via a software program having an iterative loop.One iteration of the loop is analogous to a single stage of the jointprocess estimator as shownin FIG. 8. Thus, if a loop is iterated mtimes, it is equivalent to an m stage joint process estimator 60.

[0171] A flow chart of a subroutine to estimate the primary signalportion s_(λa)(t) or the secondary signal portion n_(λa)(t) of ameasured signal, s_(λa)(t) is shown in FIG. 9. The flow chart describeshow the action of a reference processor for determining either thesecondary reference or the primary reference and the joint processestimator 60 would be implemented in software.

[0172] A one-time only initialization is performed when thephysiological monitor is turned on, as indicated by an “INITIALIZE NOISECANCELER” box 120. The initialization sets all registers 90, 92, and 96and delay element variables 110 to the values described above inequations (52) through (56).

[0173] Next, a set of simultaneous samples of the measured signalss_(λa)(t) and s_(λb)(t) is input to the subroutine represented by theflowchart in FIG. 9. Then a time update of each of the delay elementprogram variables occurs, as indicated in a “TIME UPDATE OF [Z⁻¹]ELEMENTS” box 130, wherein the value stored in each of the delay elementvariables 110 is set to the value at the input of the delay elementvariable 110. Thus, the zero-stage backward prediction error b₀(t) isstored in the first-stage delay element variable, the first-stagebackward prediction error b₁(t) is stored in the second-stage delayelement variable, and so on.

[0174] Then, using the set of measured signal samples s_(λa)(t) ands_(λb)(t), the reference signal is calculated according to theratiometric or the constant saturation method described above. This isindicated by a “CALCULATE REFERENCE [n′(t) or s′(t)) FOR TWO MEASUREDSIGNAL SAMPLES” box 140.

[0175] A zero-stage order update is performed next as indicated in a“ZERO-STAGE UPDATE” box 150. The zero-stage backward prediction errorb₀(t), and the zero-stage forward prediction error f₀(t) are set equalto the value of the reference signal n′(t) or s′(t). Additionally, theweighted sum of the forward prediction errors J_(m)(t) and the weightedsum of backward prediction errors β_(m)(t) are set equal to the valuedefined in equations (53) and (54).

[0176] Next, a loop counter, m, is initialized as indicated in a “m=0”box 160. A maximum value of m, defining the total number of stages to beused by the subroutine corresponding to the flowchart in FIG. 9, is alsodefined. Typically, the loop is constructed such that it stops iteratingonce a criterion for convergence upon a best approximation to either theprimary signal or the secondary signal has been met by the joint processestimator 60. Additionally, a maximum number of loop iterations may bechosen at which the loop stops iteration. In a preferred embodiment of aphysiological monitor of the present invention, a maximum number ofiterations, m=6 to m=10, is advantageously chosen.

[0177] Within the loop, the forward and backward reflection coefficientΓ_(f,m)(t) and. Γ_(b,m)(t) register 90 and 92 values in theleast-squares lattice filter are calculated first, as indicated by the“ORDER UPDATE MTH CELL OF LSL-LATTICE” box 170 in FIG. 9. This requirescalculation of intermediate variable and signal values used indetermining register 90, 92, and 96 values in the present stage, thenext stage, and in the regression filter 80.

[0178] The calculation of regression filter register 96 valueκ_(m,λa)(t) is performed next, indicated by the “ORDER UPDATE MTH STAGEOF REGRESSION FILTER(S)” box 180. The two order update boxes 170 and 180are performed in sequence m times, until m has reached its predeterminedmaximum (in the preferred embodiment, m=6 to m=10) or a solution hasbeen converged upon, as indicated by a YES path from a “DONE” decisionbox 190. In a computer subroutine, convergence is determined by checkingif the weighted sums of the forward and backward prediction errorsJ_(m)(t) and β_(m)(t) are less than a small positive number. An outputis calculated next, as indicated by a “CALCULATE OUTPUT” box 200. Theoutput is a good approximation to either the primary signal or secondarysignal, as determined by the reference processor 26 and joint processestimator 60 subroutine corresponding to the flow chart of FIG. 9. Thisis displayed (or used in a calculation in another subroutine), asindicated by a “TO DISPLAY” box 210.

[0179] A new set of samples of the two measured signals s_(λ,a)(t) ands_(λb)(t) is input to the processor and joint process estimator 60adaptive noise canceler subroutine corresponding to the flowchart ofFIG. 9 and the process reiterates for these samples. Note, however, thatthe initialization process does not re-occur. New sets of measuredsignal samples s_(λa)(t) and s_(λb)(t) are continuously input to thereference processor 26 and joint process estimator 60 adaptive noisecanceler subroutine. The output forms a chain of samples which isrepresentative of a continuous wave. This waveform is a goodapproximation to either the primary signal waveform s_(λa)(t) or thesecondary waveform n_(λa)(t) at wavelength λa. The waveform may also bea good approximation to either the primary signal waveform s_(λb)(t) orthe secondary waveform n″_(λb)(t) at wavelength λb.

Calculation of Saturation from Correlation Ccanceler Output

[0180] Physiological monitors may use the approximation of the primarysignals s″_(λa)(t) or s″_(λb)(t) or the secondary signals n″_(λa)(t) orn″_(λb)(t) to calculate another quantity, such as the saturation of oneconstituent in a volume containing that constituent plus one or moreother constituents. Generally, such calculations require informationabout either a primary or secondary signal at two wavelengths. Forexample, the constant saturation method requires a good approximation ofthe primary signal portions s_(λa)(t) and s_(λb)(t) of both measuredsignals S_(λa)(t) and s_(λb)(t). Then, the arterial saturation isdetermined from the approximations to both signals, i.e. s″_(λa)(t) ands″_(λb)(t). The constant saturation method also requires a goodapproximation of the secondary signal portions n_(λa)(t) or n_(λb)(t).Then an estimate of the venous saturation may be determined from theapproximations to these signals i.e. n″_(λa)(t) and n″_(λb)(t).

[0181] In other physiological measurements, information about a signalat a third wavelength is necessary. For example, to find the saturationusing the ratiometric method, signals s_(λa)(t) and s_(λb)(t) are usedto find the reference signal n′(t) or s′(t). But as discussedpreviously, λa and λb were chosen to satisfy a proportionalityrelationship like that of equation (22). This proportionalityrelationship forces the two primary signal portions s_(λa)(t) ands_(λb)(t) of equations (23c) and (24c) to be linearly dependent.Generally, linearly dependent mathematical equations cannot be solvedfor the unknowns. Analogously, some desirable information cannot bederived from two linearly dependent signals. Thus, to determine thesaturation using the ratiometric method, a third signal issimultaneously measured at wavelength λc. The wavelength kc is chosensuch that the primary portion s_(λc)(t) of the measured signal s_(λc)(t)is not linearly dependent with the primary portions s_(λa)(t) ands_(λb)(t) of the measured signals s_(λa)(t) and s_(λb)(t). Since allmeasurements are taken substantially simultaneously, the secondaryreference signal n′(t) is correlated to the secondary signal portionsn_(λa), n_(λb), and n_(λc) of each of the measured signals s_(λa)(t),s_(λb)(t), and s_(λc)(t) and can be used to estimate approximations tothe primary signal portions s_(λa)(t), s_(λb)(t), and s_(λc)(t) for allthree measured signals s_(λa)(t), s_(λb)(t), and s_(λc)(t). Using theratiometric method, estimation of the ratio of signal portions s_(λa)(t)and s_(λc)(t) of the two measured signals s_(λa)(t) and s_(λc)(t),chosen correctly, is usually satisfactory to determine mostphysiological data.

[0182] A joint process estimator 60 having two regression filters 80 aand 80 b is shown in FIG. 10. A first regression filter 80 a accepts ameasured signal s_(λa)(t). A second regression filter 80 b accepts ameasured signal s_(λb)(t) or s_(λc)(t), depending whether the constantsaturation method or the ratiometric method is used to determine thereference signal n′(t) or s′(t) for the constant saturation method orn′(t) or s′(t) for the ratiometric method The first and secondregression filters 80 a and 80 b are independent. The backwardprediction error b_(m)(t) is input to each regressiorn, filter 80 a and80 b, the input for the second regression filter 80 b bypassing thefirst regression filter 80 a.

[0183] The second regression filter 80 b comprises registers 98, andsumming elements 108 arranged similarly to those in the first regressionfilter 80 a. The second regression filter 80 b operates via anadditional intermediate variable in conjunction with those defined byequations (60) through (70), i.e.:

ρ_(m,λb)(t)=λρ_(m,λb)(t−1)+{b _(m)(t)e* _(m,λb)(t)/γ_(m)(t)}; or   (71)

ρ_(m,λc)(t)=λρ_(m,λc)(t−1)+{b _(m)(t)e* _(m,λc)(t)/γ_(m)(t)}; and   (72)

ρ_(0,λb)(0)=0; or   (73)

ρ_(0,λc)(0)=0.   (74)

[0184] The second regression filter 80 b has an error signal valuedefined similar to the first regression filter error signal values,e_(m+1,λa)(t), i.e.:

e _(m+1,λb)(t)=e _(m,λb)(t)−κ*_(m,λb)(t)b _(m)(t); or   (75)

e _(m+1,λc)(t)=e _(m,λc)(t)−κ*_(m,λb)(t)b _(m)(t); and   (76)

e _(0λ,b)(t)=s _(λb)(t) for t≧0; or (77)

e _(0,λc)(t)=s _(λc)(t) for t≧0.   (78)

[0185] The second regression filter has a regression coefficientκ_(m,λb)(t) register 98 value defined similarly to the first regressionfilter error signal values, i.e.:

κ_(m,λb)(t)={ρ_(m,λb)(t)/β_(m)(t)}; or   (79)

κ_(m,λc)(t)={ρ_(mm,λc)(t)/β_(m)(t)};   (80)

[0186] These values are used in conjunction with those intermediatevariable values, signal values, register and register values defined inequations (52) through (70). These signals are calculated in an orderdefined by placing the additional signals immediately adjacent a similarsignal for the wavelength λa.

[0187] For the ratiometric method, s_(λc)(t) is input to the secondregression filter 80 b. The output of the second regression filter 80 bis then a good approximation to the primary signal s″_(λc)(t) orsecondary signal n″_(λc)(t). For the constant saturation method,s_(λb)(t) is input to the second regression filter 80 b. The output isthen a good approximation to the primary signal s″_(λb)(t) or secondarysignal s″_(λb)(t).

[0188] The addition of the second regression filter 80 b does notsubstantially change the computer program subroutine represented by theflowchart of FIG. 9. Instead of an order update of the mth stage of onlyone regression filter, an order update of the mth stage of bothregression filters 80 a and 80 b is performed. This is characterized bythe plural designation in the ″ORDER UPDATE OF m^(th) STAGE OFREGRESSION FILTER(S)” box 180 in FIG. 9. Since the regression filters 80a and 80 b operate independently, independent calculations can beperformed in the reference processor and joint process estimator 60adaptive noise canceler subroutine modeled by the flowchart of FIG. 9.

Calculation of Saturation

[0189] Once good approximations to the primary signal portions,s″_(λa)(t) and s″_(λc)(t) or the secondary signal portions n″_(λa)(t)and n″_(λc)(t) for the ratiometric method and s″_(λa)(t) and s″_(λb)(t)or n″_(λa)(t) and n″_(λc)(t) for the constant saturation method, havebeen determined by the joint process estimator 60, the saturation of A₅in a volume containing A₅ and A₆, for example, may be calculatedaccording to various known methods. Mathematically, the approximationsto the primary signals can be written:

s″ _(λa)(t)≈ε_(5,λa) c ₅ x _(5,6)(t)+ε_(6,λa) c ₆ x _(5,6)(t)+ε_(5,λa) c₃ x _(3,4)(t)+ε_(6,λa) c ₄ x _(3,4)(t)   (81a)

s″ _(λc)(t)≈ε_(5,λc) c ₅ x _(5,6)(t)+ε_(6,λc) c ₆ x _(5,6)(t)+ε_(5,λc) c₃ x _(3,4)(t)+ε_(6,λc) c ₄ x _(3,4)(t)   (82a)

[0190] for the ratiometric method using wavelengths λa and λc, andassuming that the secondary reference n′(t) is uncorrelated withx_(3,4)(t) and x_(5,6)(t). Terms involving x_(3,4)(t) and x_(5,6)(t) maythen be separated using the constant saturation method. It is importantto understand that if n′(t) is uncorrelated with x_(3,4)(t) andx_(5,6)(t), use of the ratiometric method followed by use of theconstant saturation method results in a more accurate computation of thesaturation of A₃ in the layer x_(3,4) then by use of the ratiometric orconstant saturation methods alone. In the event that n′(t) andx_(3,4)(t) are correlated the ratiometric method yields

s″ _(λa)(t)≈ε_(5,λa) c ₅ x _(5,6)(t)+ε_(6,λa) c ₆ x _(5,6)(t); and  (81b)

s″ _(λc)(t)≈ε_(5,λc) c ₅ x _(5,6)(t)+ε_(6,λc) c ₆ x _(5,6)(t).   (82b)

[0191] For the constant saturation method, the approximations to theprimary signals can be written, in terms of λa and λb, as:

s″ _(λa)(t)≈ε_(5,λa) c ₅ x _(5,6)(t)+ε_(6,λa) c ₆ x _(5,6)(t); and  (83)

s″ _(λb)(t)≈ε_(5,λb) c ₅ x _(5,6)(t)+ε_(6,λb) c ₆ x _(5,6)(t).   (84)

[0192] Equations (81b), (82b), (83) and (84) are equivalent to twoequations having three unknowns, namely c₅(t), c₆(t) and λ_(5,6)(t). Inboth the ratiometric and the constant saturation cases, the saturationcan be determined by acquiring approximations to the primary orsecondary signal portions at two different, yet proximate times t₁ andt₂ over which the saturation of A₅ in the volume containing A₅ and A₆and the saturation of A₃ in the volume containing A₃ and A₄ does notchange substantially. For example, for the primary signals estimated bythe ratiometric method, at times t₁ and t₂:

s″ _(λa)(t ₁)≈ε_(5,λa) c ₅ x _(5,6)(t ₁)+ε_(6,λa) c ₆ x _(5,6)(t ₁)  (85)

s″ _(λc)(t ₁)≈ε_(5,λc) c ₅ x _(5,6)(t ₁)+ε_(6,λc) c ₆ x _(5,6)(t ₁)  (86)

s″ _(λa)(t ₂)≈ε_(5,λa) c ₅ x _(5,6)(t ₂)+ε_(6,λa) c ₆ x _(5,6)(t ₂)  (87)

s″ _(λc)(t ₁)≈ε_(5,λc) c ₅ x _(5,6)(t ₁)+ε_(6,λc) c ₆ x _(5,6)(t ₁)  (88)

[0193] Then, difference signals may be determined which relate thesignals of equations (85) through (88), i.e.:

Δs _(λa) =s″ _(λa)(t ₁)−s″ _(λa)(t ₂)≈ε_(5,λa) c ₅ Δx+ε _(6,λa) c ₆ Δx;and   (89)

Δs _(λc) =s″ _(λc)(t ₁)−s″ _(λc)(t ₂)≈ε_(5,λc) c ₅ Δx+ε _(6,λc) c ₆ Δx;  (90)

[0194] where Δx=x_(5,6)(t₁)−x_(5,6)(t₂). The average saturation at timet=(t₁+t₂)/2 is: $\begin{matrix}{{{Saturation}(t)} = {{c_{5}(t)}/\left\lbrack {{c_{5}(t)} + {c_{6}(t)}} \right\rbrack}} & (91) \\{\quad {= \frac{ɛ_{6,{\lambda \quad a}} - {ɛ_{6,{\lambda \quad c}}\left( {\Delta \quad {s_{\lambda \quad a}/\Delta}\quad s_{\lambda \quad c}} \right)}}{ɛ_{6,{\lambda \quad a}} - ɛ_{5,{\lambda \quad a}} - {\left( {ɛ_{6,{\lambda \quad c}} - ɛ_{5,{\lambda \quad c}}} \right)\left( {\Delta \quad {s_{\lambda \quad a}/\Delta}\quad s_{\lambda \quad c}} \right)}}}} & (92)\end{matrix}$

[0195] It will be understood that the Ax term drops out from thesaturation calculation because of the division. Thus, knowledge of thethickness of the primary constituents is not required to calculatesaturation.

Pulse Oximetry Measurements

[0196] A specific example of a physiological monitor utilizing aprocessor of the present invention to determine a secondary referencen′(t) for input to a correlation canceler that removes erraticmotion-induced secondary signal portions is a pulse oximeter. Pulseoximetry may also be performed utilizing a processor of the presentinvention to determine a primary signal reference s′(t) which may beused for display purposes or for input to a correlation canceler toderive information about patient movement and venous blood oxygensaturation.

[0197] A pulse oximeter typically causes energy to propagate through amedium where blood flows close to the surface for example, an ear lobe,or a digit such as a finger, or a forehead. An attenuated signal ismeasured after propagation through or reflected from the medium. Thepulse oximeter estimates the saturation of oxygenated blood.

[0198] Freshly oxygenated blood is pumped at high pressure from theheart into the arteries for use by the body. The volume of blood in thearteries varies with the heartbeat, giving rise to a variation inabsorption of energy at the rate of the heartbeat, or the pulse.

[0199] Oxygen depleted, or deoxygenated, blood is returned to the heartby the veins along with unused oxygenated blood. The volume of blood inthe veins varies with the rate of breathing, which is typically muchslower than the heartbeat. Thus, when there is no motion inducedvariation in the thickness of the veins, venous blood causes a lowfrequency variation in absorption of energy. When there is motioninduced variation in the thickness of the veins, the low frequencyvariation in absorption is coupled with the erratic variation inabsorption due to motion artifact.

[0200] In absorption measurements using the transmission of energythrough a medium, two light emitting diodes (LED's) are positioned onone side of a portion of the body where blood flows close to thesurface, such as a finger, and a photodetector is positioned on theopposite side of the finger. Typically, in pulse oximetry measurements,one LED emits a visible wavelength, preferably red, and the other LEDemits an infrared wavelength. However, one skilled in the art willrealize that other wavelength combinations could be used.

[0201] The finger comprises skin, tissue, muscle, both arterial bloodand venous blood, fat, etc., each of which. absorbs light energydifferently due to different absorption coefficients, differentconcentrations, and different thicknesses. When the patient is notmoving, absorption is substantially constant except for the flow ofblood. The constant attenuation can be determined and subtracted fromthe signal via traditional filtering techniques. When the patient moves,the absorption becomes erratic. Erratic motion induced noise typicallycannot be predetermined and/or subtracted from the measured signal. viatraditional filtering techniques. Thus, determining the oxygensaturation of arterial blood and venous blood becomes more difficult.

[0202] A schematic of a physiological monitor for pulse oximetry isshown in FIG. 11. Two LED's 300 and 302, one LED 300 emitting redwavelengths and another LED 302 emitting infrared wavelengths, areplaced adjacent a finger 310. A photodetector 320, which produces anelectrical signal corresponding to the attenuated visible and infraredlight energy signals is located opposite the LED's 300 and 302. Thephotodetector 320 is connected to a single channel of common processingcircuitry including an amplifier 330 which is in turn connected to aband pass filter 340. The band pass filter 340 passes it output signalinto a synchronized demodulator 350 which has a plurality of outputchannels. One output channel is for signals corresponding to visiblewavelengths and another output channel is for signals corresponding toinfrared wavelengths.

[0203] The output channels of the synchronized demodulator for signalscorresponding to both the visible and infrared wavelengths are eachconnected to separate paths, each path comprising further processingcircuitry. Each path includes a DC offset removal element 360 and 362,such as a differential amplifier, a programmable gain amplifier 370 and372 and a low pass filter 380 and 382. The output of each low passfilter 380 and 382 is amplified in a second programmable gain amplifier390 and 392 and then input to a multiplexer 400.

[0204] The multiplexer 400 is connected to an analog-to-digitalconverter 410 which is in turn connected to a microprocessor 420.Control lines between the microprocessor 420 and the multiplexer 400,the microprocessor 420 and the analog-to-digital converter 410, and themicroprocessor 420 and each programmable gain amplifier 370, 372, 390,and 392 are formed. The microprocessor 420 has additional control lines,one of which leads to a display 430 and the other of which leads to anLED driver 440 situated in a feedback loop with the two LED's 300 and302.

[0205] The LED's 300 and 302 each emits energy which is absorbed by thefinger 310 and received by the photodetector 320. The photodetector 320produces an electrical signal which corresponds to the intensity of. thelight energy striking the photodetector 320 surface. The amplifier 330amplifies this electrical signal for ease of processing. The band passfilter 340 then removes unwanted high and low frequencies. Thesynchronized demodulator 350 separates the electrical signal intoelectrical signals corresponding to the red and infrared light energycomponents. A predetermined reference voltage, V_(ref), is subtracted bythe DC offset removal element 360 and 362 from each of the separatesignals to remove substantially constant absorption which corresponds toabsorption when there is no motion induced signal component. Then thefirst programmable gain amplifiers 370 and 372 amplify each signal forease of manipulation. The low pass filters 380 and 382 integrate eachsignal to remove unwanted high frequency components and the secondprogrammable gain amplifiers 390 and 392 amplify each signal for furtherease of processing.

[0206] The multiplexer 400 acts as an analog switch between theelectrical signals corresponding to the red and the infrared lightenergy, allowing first a signal corresponding to the red light to enterthe analog-to-digital converter 410 and then a signal corresponding tothe infrared light to enter the analog-to-digital converter 410. Thiseliminates the need for multiple analog-to-digital converters 410. Theanalog-to-digital converter 410 inputs the data into the microprocessor420 for calculation of either a primary or secondary reference signalvia the processing technique of the present invention and removal orderivation of motion induced signal portions via a correlation canceler,such as an adaptive noise canceler. The microprocessor 420 centrallycontrols the multiplexer 400, the analog-to-digital converter 410, andthe first and second programmable gain amplifiers 370 and 390 for boththe red and the infrared channels. Additionally, the microprocessor 420controls the intensity of the LED's 302 and 304 through the LED-driver440 in a servo loop to keep the average intensity received at thephotodetector 320 within an appropriate range. Within the microprocessor420 a reference signal n′(t) or s′(t) is calculated via either theconstant saturation method or the ratiometric method, as describedabove, the constant saturation method being generally preferred. Thissignal is used in an adaptive noise canceler of the joint processestimator type 60, as described above.

[0207] The multiplexer 400 time multiplexes, or sequentially switchesbetween, the electrical signals corresponding to the red and theinfrared light energy. This allows a single channel to be used to detectand begin processing the electrical signals. For example, the red LED300 is energized first and the attenuated signal is measured at thephotodetector 320. An electrical signal corresponding to the intensityof the attenuated red light energy is passed to the common processingcircuitry. The infrared LED 302 is energized next and the attenuatedsignal is measured at the photodetector 320. An electrical signalcorresponding to the intensity of the attenuated infrared light energyis passed to the common processing circuitry. Then, the red LED 300 isenergized again and the corresponding electrical signal is passed to thecommon processing circuitry. The sequential energization of LED's 300and 302 occurs continuously while the pulse oximeter is operating.

[0208] The processing circuitry is divided into distinct paths after thesynchronized demodulator 350 to ease time constraints generated by timemultiplexing. In the preferred embodiment of the pulse oximeter shown inFIG. 11, a sample rate, or LED energization rate, of 625 Hz isadvantageously employed. Thus, electrical signals reach the synchronizeddemodulator 350 at a rate of 625 Hz. Time multiplexing is not used inplace of the separate paths due to settling time constraints of the lowpass filters 380, 382, and 384.

[0209] In FIG. 11, a third LED 304 is shown adjacent the finger, locatednear; the LED's 300 and 302. The third LED 304 is used tog measure athird signal s_(λc)(t) to be used to determine saturation using theratiometric method. The third LED 304 is time multiplexed with the redand infrared LED's 300 and 302. Thus, a third signal is input to thecommon processing circuitry in sequence with the signals from the redand infrared LED's 300 and 302. After passing through and beingprocessed by the operational amplifier 330, the band pass filter 340,and the synchronized demodulator 350, the third electrical signalcorresponding to light energy at wavelength λc is input to a separatepath including a DC offset removal element 364, a first programmablegain amplifier 374, a low pass filter 384, and a second programmablegain amplifier 394. The third signal is then input to the multiplexer400.

[0210] The dashed line connection for the third LED 304 indicates thatthis third LED 304 is incorporated into the pulse oximeter when theratiometric method is used; it is unnecessary for the constantsaturation method. When the third LED 304 is used, the multiplexer 400acts as an analog switch between all three LED 300, 302, and 304signals. If the third LED 304 is utilized, feedback loops between themicroprocessor 420 and the first and second programmable gain amplifier374 and 394 in the λc wavelength path are also formed.

[0211] For pulse oximetry measurements using the ratiometric method, thesignals (logarithm converted) transmitted through the finger 310 at eachwavelength λa, λb, and λc are:

s _(λa)(t)=s _(λred1)(t)=ε_(Hb02,λa) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λa)c ^(A) _(Hb) x ^(A)(t)+ε_(Hb02,λa) c ^(V) _(Hb02) x ^(V)(t)+ε_(Hb,λa) c^(V) _(Hb) x ^(V)(t)+n _(λa)(t).   (93)

s _(λb)(t)=s _(λred2)(t)=ε_(Hb02,λb) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λb)c ^(A) _(Hb) x ^(A)(t)+ε_(Hb02,λb) c ^(V) _(Hb02) x ^(V)(t)+ε_(Hb,λb) c^(V) _(Hb) x ^(V)(t)+n _(λb)(t).   (94)

s _(λc)(t)=s _(λIR)(t)=ε_(Hb02,λc) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λc) c^(A) _(Hb) x ^(A)(t)+ε_(Hb02,λc) c ^(V) _(Hb02) x ^(V)(t)+ε_(Hb,λc) c^(V) _(Hb) x ^(V)(t)+n _(λc)(t).   (95)

[0212] In equations (93) through (95), x^(A)(t) is the lump-sumthickness of the arterial blood in the finger; x^(V)(t) is the lump-sumthickness of venous blood in the finger; ε_(Hb02,λa)ε_(Hb02,λb, ε)_(Hb02,λc), ε_(Hb,λa), ε_(Hb,λb), and ε_(Hb,λc) are the absorptioncoefficients of the oxygenated and non-oxygenated hemoglobin, at eachwavelength measured; and c_(Hb02)(t) and c_(Hb)(t) with the superscriptdesignations A and V are the concentrations of the oxygenated andnon-oxygenated arterial blood and venous blood, respectively.

[0213] For the ratiometric method, the wavelengths chosen are typicallytwo in the visible red range, i.e., λa and λb, and one in the infraredrange, i.e., λc. As described above, the measurement wavelengths λa andλb are advantageously chosen to satisfy a proportionality relationshipwhich removes the primary signal portions s_(λa)(t) and s_(λb)(t),yielding a secondary reference n′(t). In the preferred embodiment, theratiometric method is used to determine. the secondary reference signaln′(t) by picking two wavelengths that cause the primary portionss_(λa)(t) and s_(λb)(t) of the measured signals s_(λa)(t) and S_(λb)(t)to become linearly dependent similarly to equation (22); i.e.wavelengths λa and λb which satisfy:

ε_(Hb02,λa)/ε_(Hb,λa)=ε_(Hb02,λb)/ε_(Hb,λb)   (96)

[0214] Typical wavelength values chosen are λa=650 nm and λb=685 nm.Additionally a typical wavelength value for λc is λc=940 nm. By pickingwavelengths λa and λb to satisfy equation (96) the venous portion of themeasured signal is also caused to become linearly dependent even thoughit is not usually considered to be part of the primary signals as is thecase in the constant saturation method. Thus, the venous portion of thesignal is removed with the primary portion of the constant saturationmethod. The proportionality relationship between equations (93) and (94)which allows determination of a non-zero secondary reference signaln′(t), similarly to equation (25) is:

ω_(av) =ε _(Hb,λa)/ε_(Hb,λb); where   (97)

n _(λa)(t)≠ω_(av) n _(λb)(t)   (98)

[0215] In pulse oximetry, both equations (97) and (98) can typically besatisfied simultaneously.

[0216]FIG. 12 is a graph of the absorption coefficients of oxygenatedand deoxygenated hemoglobin (ε_(Hb02) and ε_(Hb)) vs. wavelength (λ).FIG. 13 is a graph of the ratio of the absorption coefficients vs.wavelength, i.e., ε_(Hb)/ε_(Hb02) vs. over the range of wavelengthwithin circle 13 in FIG. 12. Anywhere a horizontal line touches thecurve of FIG. 13 twice, as does line 400, the condition of equation (96)is satisfied. FIG. 14 shows an exploded view of the area of FIG. 12within the circle 13. Values of ε_(Hb02) and ε_(Hb) at the wavelengthswhere a horizontal line touches the curve of FIG. 13 twice can then bedetermined from the data in FIG. 14 to solve for the proportionalityrelationship of equation (97).

[0217] A special case of the ratiometric method is when the absorptioncoefficients ε_(Hb02) and ε_(Hb) are equal at a wavelength. Arrow 410 inFIG. 12 indicates one such location, called an isobestic point. FIG. 14shows an exploded view of the isobestic point. To use isobestic pointswith the ratiometric method, two wavelengths at isobestic points aredetermined to satisfy equation (96)

[0218] Multiplying equation (94) by ω_(av) and then subtracting equation(94) from equation (93), a non-zero secondary reference signal n′(t) isdetermined by:

n′(t)=s _(λa)(t)−ω_(av) s _(λb)(t)=n _(λa)(t)−ω_(av) n _(λb).   (99)

[0219] This secondary reference signal n′(t) has spectral contentcorresponding to the erratic, motion-induced noise. When it is input toa correlation canceler, such as an adaptive noise canceler, with eitherthe signals s_(λa)(t) and s_(λc)(t) or s_(λb)(t) and s_(λc)(t) input totwo regression filters 80 a and 80 b as in FIG. 10, the adaptive noisecanceler will function much like an adaptive multiple notch filter andremove frequency components present in both the secondary referencesignal n′(t) and the measured signals from the measured signalss_(λa)(t) and s_(λc)(t) or s_(λb)(t) and s_(λc)(t). If the secondaryreference signal n′(t) is correlated to the venous portion, then theadaptive noise canceler is able to remove erratic noise caused in thevenous portion of the measured signals s_(λa)(t), s_(λb)(t), ands_(λc)(t) even though the venous portion of the measured signalss_(λa)(t) and s_(λb)(t) was not incorporated in the secondary referencesignal n′(t). In the event that the secondary reference signal n′(t) isnot correlated to the venous component, then, the adaptive noisecanceler generally will not remove the venous portion from the measuredsignals. However, a band pass filter applied to the approximations tothe primary signals S″_(λa)(t) and s″_(λc)(t) or s″_(λb)(t) ands″_(λc)(t) can remove the low frequency venous signal due to breathing.

[0220] For pulse oximetry measurements using the constant saturationmethod, the signals (logarithm converted) transmitted through the finger310 at each wavelength λa and λb are:

s _(λa)(t)=s _(λred1)(t)=ε_(Hb02,λa) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λa)c ^(A) _(Hb) x ^(A)(t)+ε_(Hb02,λa) c ^(V) _(Hb02) x ^(V)(t)+ε_(Hb,λa) c^(V) _(Hb) x ^(V)(t)+n _(λa)(t).   (100a)

s _(λa)(t)=ε_(Hb02,λa) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λa) c ^(A) _(Hb) x^(A)(t)+n _(λa)(t)   (100b)

s _(λa)(t)=s _(λa)(t)+n _(λa)(t)   (100c)

s _(λb)(t)=s _(λred2)(t)=ε_(Hb02,λb) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λb)c ^(A) _(Hb) x ^(A)(t)+ε_(Hb02,λb) c ^(V) _(Hb02) x ^(V)(t)+ε_(Hb,λb) c^(V) _(Hb) x ^(V)(t)+n _(λb)(t).   (101a)

s _(λb)(t)=ε_(Hb02,λb) c ^(A) _(Hb02) x ^(A)(t)+ε_(Hb,λb) c ^(A) _(Hb) x^(A)(t)+n _(λb)(t)   (101b)

s _(λb)(t)=s _(λb)(t)+n _(λb)(t)   (101c)

[0221] For the constant saturation method, the wavelengths chosen aretypically one in the visible red range, i.e., λa, and one in theinfrared range, i.e., λb. Typical wavelength values chosen are λa=660 nmand λb=940 nm. Using the constant saturation method, it is assumed thatc^(A) _(Hb02)(t)/c^(A) _(Hb)(t)=constant₁ and c^(V) _(Hb02)(t)/c^(V)_(Hb)(t)=constant₂. The oxygen saturation of arterial and venous bloodchanges slowly, if at all, with respect to the sample rate, making thisa valid assumption. The proportionality factors for equations (100) and(101) can then be written as: $\begin{matrix}{{\omega_{a}(t)} = \frac{{ɛ_{{HbO2},{\lambda \quad a}}c_{HbO2}^{A}{x(t)}} + {ɛ_{{Hb},{\lambda \quad a}}c_{Hb}^{A}{x(t)}}}{{ɛ_{{HbO2},{\lambda \quad b}}c_{HbO2}^{A}{x(t)}} + {ɛ_{{Hb},{\lambda \quad b}}c_{Hb}^{A}{x(t)}}}} & (102) \\{{s_{\lambda \quad a}(t)} = {{\omega_{a}(t)}\quad {s_{\lambda \quad b}(t)}}} & \left( {103a} \right) \\{{n_{\lambda \quad a}(t)} \neq {{\omega_{a}(t)}\quad {n_{\lambda \quad b}(t)}}} & \left( {104a} \right) \\{{n_{\lambda \quad a}(t)} = {{\omega_{v}(t)}{n_{\lambda \quad b}(t)}}} & \left( {103b} \right) \\{{s_{\lambda \quad a}(t)} \neq {{\omega_{v}(t)}{s_{\lambda \quad b}(t)}}} & \left( {104b} \right)\end{matrix}$

[0222] In pulse oximetry, it is typically the case that both equations(103) and (104) can be satisfied simultaneously.

[0223] Multiplying equation (101) by ω_(a)(t) and then subtractingequation (101) from equation (100), a non-zero secondary referencesignal n′(t) is determined by: $\begin{matrix}{{n^{\prime}(t)} = {{S_{\lambda \quad a}(t)} - {{\omega_{a}(t)}\quad {S_{\lambda \quad b}(t)}}}} & \left( {105a} \right) \\{\quad {= {{ɛ_{{HbO2},{\lambda \quad a}}c_{HbO2}^{V}{x^{V}(t)}} + {ɛ_{{Hb},{\lambda \quad a}}c_{Hb}^{V}{x^{V}(t)}} + {n_{\lambda \quad a}(t)} -}}} & \left( {106a} \right) \\{\quad {{{\omega_{a}(t)}\quad\left\lbrack {{ɛ_{{HbO2},{\lambda \quad b}}c_{HbO2}^{V}{x^{V}(t)}} + {ɛ_{{Hb},{\lambda \quad b}}c_{Hb}^{V}{x^{V}(t)}} + {n_{\lambda \quad b}(t)}} \right\rbrack}.}} & \quad\end{matrix}$

[0224] Multiplying equation (101) by ω_(v)(t) and then subtractingequation (101) from equation (100), a non-zero primary reference signals′(t) is determined by: $\begin{matrix}{{s^{\prime}(t)} = {{S_{\lambda \quad a}(t)} - {{\omega_{v}(t)}\quad {S_{\lambda \quad b}(t)}}}} & \left( {105b} \right) \\{\quad {= {{s_{\lambda \quad a}(t)} - {{\omega_{v}(t)}\quad {s_{\lambda \quad b}(t)}}}}} & \left( {106b} \right)\end{matrix}$

[0225] The constant saturation assumption does not cause the vehouscontribution to the absorption to be canceled along with the primarysignal portions s_(λa)(t) and s_(λb)(t), as did the relationship ofequation (96) used in the ratiometric method. Thus, frequenciesassociated with both the low frequency modulated absorption due tovenous absorption when the patient is still and the erraticallymodulated absorption due to venous absorption when the patient is movingare represented in the secondary reference signal n′(t). Thus, thecorrelation canceler can remove or derive both erratically modulatedabsorption due to venous blood in the finger under motion and theconstant low frequency cyclic absorption of venous blood.

[0226] Using either method, a primary reference s′(t) or a secondaryreference n′(t) is determined by the processor of the present inventionfor use in a correlation canceler, such as an adaptive noise canceler,which is defined by software in the microprocessor. The preferredadaptive noise canceler is the joint process estimator 60 describedabove.

[0227] Illustrating the operation of the ratiometric method of thepresent invention, FIGS. 15, 16 and 17 show signals measured for use indetermining the saturation of oxygenated arterial blood using areference processor of the present invention which employs theratiometric method, i.e., the signals s_(λa)(t)=s_(λred1)(t),s_(λb)(t)=s_(λred2)(t), and s_(λc)(t)=s_(λIR)(t). A first segment 15 a,16 a, and 17 a of each of the signals,is relatively undisturbed bymotion artifact, i.e., the patient did not move substantially during thetime period in which these segments were measured. These segments 15 a,16 a, and 17 a are thus generally representative of the plethysmographicwaveform at each of the measured wavelengths. These waveforms are takento be the primary signals s_(λa)(t), s_(λb)(t), and s_(λc)(t). A secondsegment 15 b, 16 b, and 17 b of each of the signals is affected bymotion artifact, i.e., the patient did move during the time period inwhich these segments were measured. Each of these segments 15 b, 16 b,and 17 b shows large motion induced excursions in the measured signalThese waveforms contain both primary plethysmographic signals andsecondary motion induced excursions. A third segment 15 c, 16 c, and 17c of each of the signals is again relatively unaffected by motionartifact and is thus generally representative of the plethysmographicwaveform at each of the measured wavelengths.

[0228]FIG. 18 shows the secondary reference signaln′(t)=n_(λa)−ω_(av)n_(λb)(t), as determined by a reference processor ofthe present invention utilizing the ratiometric method. As discussedpreviously, the secondary reference signal n′(t) is correlated to thesecondary signal portions n_(λa), n_(λb), and n_(λc). Thus, a firstsegment 18 a of the secondary reference signal n′(t) is generally flat,corresponding to the fact that there is very little motion induced noisein the first segments 15 a, 16 a, and 17 a of each signal. A secondsegment 18 b of the secondary reference signal n′(t) exhibits largeexcursions, corresponding to the large motion induced excursions in eachof the measured signals. A third segment 18c of the secondary referencesignal n′(t) is generally flat, again corresponding to the lack ofmotion artifact in the third segments 15 c, 16 c, and 17 c of eachmeasured signal.

[0229]FIG. 19 shows the primary reference signal s′(t)=s_(λa)−ω_(e) s_(λb)(t), as determined by a reference processor of the presentinvention utilizing the ratiometric method. As discussed previously, theprimary reference signal s′(t) is correlated to the primary signalportions s_(λa)(t), s_(λb)(t), and s_(λc)(t). Thus, a first segment 19 aof the primary reference signal s′(t) is indicative of theplethysmographic waveform, corresponding to the fact that there is verylittle motion induced noise in the first segments 15 a, 16 a, and 17 aof each signal. A second segment 19 b of the primary reference signals′(t) also exhibits a signal related to a plethymographic waveform,corresponding to each of the measured signals in the absence of thelarge motion induced excursions. A third segment 19 c of the primaryreference signal s′(t) is generally indicative of the plethysmographicwaveform, again corresponding to the lack of motion artifact in thethird segments 15 c, 16 c, and 17 c of each measured signal.

[0230]FIGS. 20 and 21 show the approximations s″_(a)(t) and s″_(λc)(t)to the primary signals s_(λa)(t) and s_(λc)(t) as estimated by thecorrelation canceler 27 using a secondary reference signal n′(t)determined by the ratiometric method. FIGS. 20 and 21 illustrate theeffect of correlation cancelation using the secondary reference signaln′(t) as determined by the reference processor of the present inventionusing the ratiometric method. Segments 20 b and 21 b are not dominatedby motion induced noise as were segments 15 b, 16 b, and 17 b of themeasured signals. Additionally, segments 20 a, 21 a, 20 c, and 21 c havenot been substantially changed from the measured signal segments 15 a,17 a, 15 c, and 17 c where there was no motion induced noise.

[0231]FIGS. 22 and 23 show the approximations n″_(λa)(t) and n″_(λc)(t)to the primary signals n_(λa)(t) and n_(λc)(t) as estimated by thecorrelation canceler 27 using a primary reference signal s′(t)determined by the ratiometric method. Note that the scale of FIGS. 15through 23 is not the same for each figure to better illustrate changesin each signal. FIGS. 22 and 23 illustrate the effect of correlationcancelation using the primary reference signal s′(t) as determined bythe reference processor of the present invention using the ratiometricmethod. Only segments 22 b and 23b are dominated by motion induced noiseas were segments 15 b, 16 b, and 17 b of the measured signals.Additionally, segments 22 a, 23 a, 22 c, and 23 c are nearly zerocorresponding to the measured signal segments 15 a, 17 a, 15 c, and 17 cwhere there was no motion induced noise.

[0232] Illustrating the operation of the constant saturation method ofthe present invention, FIGS. 24 and 25 show signals measured for inputto a reference processor of the present invention which employs theconstant saturation methodi i.e., the signals s_(λa)(t)=s_(red)(t) ands_(λb)(t)=s_(λIR)(t). A first segment 24 a and 25 a of each of thesignals is relatively undisturbed by motion artifact, i.e., the patientdid not move substantially during the time period in which thesesegments were measured. These segments 24 a and 25 a are thus generallyrepresentative of the primary plethysmographic waveform at each of themeasured wavelengths. A second segment 24 b and 25 b of each of thesignals is affected by motion artifact, i.e., the patient did moveduring the time period in which these segments were measured. Each ofthese segments 24 b and 25 b shows large motion induced excursions inthe measured signal. A third segment 24 c and 25 c of each of thesignals is again relatively unaffected by motion artifact and is thusgenerally representative of the primary No plethysmographic waveform ateach of the measured wavelengths.

[0233]FIG. 26 shows the secondary reference signaln′(t)=n_(λa)(t)−ω_(a)n_(λb)(t), as determined by a reference processorof the present invention utilizing the constant saturation method.Again, the secondary reference signal n′(t) is correlated to thesecondary signal portions n_(λa) and n_(λb). Thus, a first segment 26 aof the secondary reference signal n′(t) is generally flat, correspondingto the fact that there is very little motion induced noise in the firstsegments 24 a and 25 a of each signal. A second segment 26 b of thesecondary reference signal n′(t) exhibits large excursions,corresponding to the large motion induced excursions in each of themeasured signals. A third segment 26 c of the noise reference signaln′(t) is generally flat, again corresponding to the lack of motionartifact in the third segments 24 c and 25 c of each measured signal.

[0234]FIG. 27 shows the primary reference signals′(t)=s_(λa)−ω_(v)s_(λb)(t), as determined by a reference processor ofthe present invention utilizing the constant saturation method. Asdiscussed previously, the primary reference signal s′(t) is correlatedto the primary signal portions s_(λa)(t) and s_(λb)(t). Thus, a firstsegment 27 a of the primary reference signal s′(t) is indicative of theplethysmographic waveform, corresponding to the fact that there is verylittle motion induced noise in the first segments 24 a and 25 a of eachsignal. A second segment 27 b of the primary reference signal s′(t) alsoexhibits a signal related to a plethymographic waveform, correspondingto each of the measured signals in the absence of the large motioninduced excursions. A third segment 27 c of the primary reference signals′(t) is generally indicative of the plethysmographic waveform, againcorresponding to the lack of motion artifact in the third segments 24 cand 25 c of each measured signal.

[0235]FIGS. 28 and 29 show the approximations s″_(λa)(t) and s″_(λb)(t)to the primary signals s_(λa)(t) and s_(λb)(t) as estimated by thecorrelation canceler 27 using a secondary reference signal n′(t)determined by the constant saturation method. FIGS. 28 and 29 illustratethe effect of correlation cancelation using the secondary referencesignal n′(t) as determined by a reference processor of the presentinvention utilizing the constant saturation method. Segments 28 b and 28b are not dominated by motion induced noise as were segments 24 b and 25b of the measured signals. Additionally, segments 28 a, 29 a, 28 c, and29 c have not been substantially changed from the measured signalsegments 24 a, 25 a, 24 c, and 25 c where there was no motion inducednoise.

[0236]FIGS. 30 and 31 show the approximations n″_(λa)(t) and n″_(λb)(t)to the secondary signals n_(λa)(t) and n_(λb)(t) as estimated by thecorrelation canceler 27 using a primary reference signal s′(t)determined by the constant saturation method. Note that the scale ofFIGS. 24 through 31 is not the same for each figure to better illustratechanges in each signal. FIGS. 30 and 31 illustrate the effect ofcorrelation cancelation using the primary reference signal s′(t) asdetermined by a reference processor of the present invention utilizingthe constant saturation method. Only segments 30 b and 31 b aredominated by motion induced noise as were segments 24 b, and 25 b of themeasured signals. Additionally, segments 30 a, 31 a, 30 c, and 31 c arenearly zero corresponding to the measured signal segments 24 a, 25 a, 24c, and 25 c where there was no motion induced noise.

Method for Estimating Primary and Secondary Signal Portions of MeasuredSignals in a Pulse Oximeter

[0237] A copy of a computer subroutine, written in the C programminglanguage, calculates a primary reference s′(t) and a secondary referencen′(t) using the ratiometric method and, using a joint. process estimator60, estimates either the primary or secondary signal portions of twomeasured signals, each having a primary signal which is correlated withthe primary reference s′(t) and having a secondary signal which iscorrelated with the secondary reference n′(t), is appended in AppendixA. For example, s_(λa)(t)=s_(λred)(t)=s_(λ660 nm)(t) ands_(λb)(t)=s_(λIR)(t)=s_(λ940 nm)(t) can be input to the computersubroutine. This subroutine is one way to implement the stepsillustrated in the flowchart of FIG. 9 for a monitor particularlyadapted for pulse oximetry.

[0238] The program estimates either the primary signal portions or thesecondary signal portions of two light energy signals, one preferablycorresponding to light in the visible red range and the other preferablycorresponding to light in the infrared range such that a determinationof the amount of oxygen, or the saturation of oxygen in the arterial andvenous blood components, may be made. The calculation of the saturationis performed in a separate subroutine.

[0239] Using the ratiometric method three signals s_(λa)(t), s_(λb)(t)and s_(λc)(t) are input to the subroutine. s_(λa)(t) and s_(λb)(t) areused to calculate either the primary or secondary reference signal s′(t)or n′(t). As described above, the wavelengths of light at whichs_(λa)(t) and s_(λb)(t) are measured are chosen to satisfy therelationship of equation (96). Once either the secondary referencesignal n′(t) or the primary reference signal s′(t) is determined, eitherthe primary signal portions s_(λa)(t) and s_(λc)(t) or the secondarysignal portions n_(λa)(t) and n_(λc)(t) of the measured signalss_(λa)(t) and s_(λc)(t) are estimated for use in calculation of theoxygen saturation.

[0240] The correspondence of the program variables to the variablesdefined in the discussion of the joint process estimator is as follows:

Δ_(m)(t)=nc[m].Delta

Γ_(f,m)(t)=nc[m].fref

Γ_(b,m)(t)=nc[m].bref

f _(m)(t)=nc[m].ferr

b _(m)(t)=nc[m].berr

J _(m)(t)=nc[m].Fswsqr

β_(m)(t)=nc[m].Bswsqr

γ_(m)(t)=nc[m].Gamma

ρ_(m,λa)(t)=nc[m].Roh _(—) a

ρ_(m,λc)(t)=nc[m].Roh _(—) c

e _(m,λa)(t)=nc[m].err _(—) a

e _(m,λc)(t)=nc[m].err _(—) c

κ_(m,λa)(t)=nc[m].K _(—) a

κ_(m,λc)(t)=nc[m].K _(—) c

[0241] A first portion of the program performs the initialization of theregisters 90, 92, 96, and 98 and intermediate variable values as in the“INITIALIZE CORRELATION CANCELER” box 120 and equations (52) through(56) and equations (73), (74), (77), and (78). A second portion of theprogram performs the time updates of the delay element variables 110where the value at the input of each delay element variable 110 isstored in the delay element variable 110 as in the “TIME UPDATE OF [Z⁻¹]ELEMENTs” box 130.

[0242] A third portion of the program calculates the reference signal,as in the “CALCULATE SECONDARY REFERENCE (n′(t)) oR PRIMARY REFERENCE(s′(t)) fOR TWO MEASURED SIGNAL SAMPLEs” box 140 using theproportionality constant cav determined by the ratiometric method as inequation (25).

[0243] A fourth portion of the program performs the zero-stage update asin the “ZERO-STAGE UPDATE” box 150 where the zero-stage forwardprediction error f_(o)(t) and the zero-stage backward prediction errorb_(o)(t) are set equal to the value of the reference signal n′(t) ors′(t) just calculated. Additionally, zero-stage values of intermediatevariables J₀(t) and β₀(t) (nc[m].Fswsqr and nc[m].Bswsqr in the program)are calculated for use in setting register 90, 92, 96, and 98 values inthe least-squares lattice predictor 70 and the regression filters 80 aand 80 b:

[0244] A fifth portion of the program is an iterative loop wherein theloop counter, m, is reset to zero with a maximum of m=NC_CELLS, as inthe “m=0” box 160 in FIG. 9. NC_CELLS is a predetermined maximum valueof iterations for the loop. A typical value of NC_CELLS is between 6 and10, for example. The conditions of the loop are set such that the loopiterates a minimum of five times and continues to iterate until a testfor conversion is met or m=NC_CELLS. The test for conversion is whetheror not the sum of the weighted sum of forward prediction errors plus theweighted sum of backward prediction errors is less than a small number,typically 0.00001 (i.e, J_(m)(t)+β_(m)(t)≦0.00001).

[0245] A sixth portion of the program calculates the forward andbackward reflection coefficient Γ_(m,f)(t) and Γ_(m,b)(t) register 90and 92 values (nc[m].fref and nc[m].bref in the program) as in the“ORDER UPDATE m^(th)-STAGE OF LSL-PREDICTOR” box 170 and equations (61)and (62). Then forward and backward prediction errors f_(m)(t) andb_(m)(t) (nc[m].ferr and nc[m].berr in the program) are calculated as inequations (63) and (64). Additionally, intermediate variables J_(m)(t),β_(m)(t) and γ_(m)(t) (nc[m].Fswsqr, nc[m].Bswsqr, nc[m].Gamma in theprogram) are calculated, as in equations (65), (66), and (67). The firstcycle of the loop uses the values for nc[0].Fswsqr and nc[0].Bswsqrcalculated in the ZERO-STAGE UPDATE portion of the program.

[0246] A seventh portion of the program, still within the loop,calculates the regression coefficient κ_(M,λa)(t) and κ_(m,λc)(t)register 96 and 98 values (nc[m].K_a and nc[m].K_c in the program) inboth regression filters, as in the “ORDER UPDATE m^(th) STAGE OFREGRESSION FILTER(S)” box 180 and equations (68) through (80).Intermediate error signals and variables e_(m,λa)(t), e_(m,λc)(t),ρ_(m,λa)(t), and ρ_(m,λc)(t) (nc[m].err_a and nc[m].err_c, nc[m].roh_a,and nc[m].roh_c in the subroutine) are also calculated as in equations(75), (76), (71), and (72), respectively.

[0247] The test for convergence of the joint process estimator isperformed each time the loop iterates, analogously to the “DONE” box190. If the sum of the weighted sums of the forward and backwardprediction errors J_(m)(t)+β_(m)(t) is less than or equal to 0.00001,the loop terminates. Otherwise, the sixth and seventh portions of theprogram repeat.

[0248] When either the convergence test is passed or m=NC_CELLS, aneighth portion of the program calculates the output of the joint processestimator 60 as in the “CALCULATE OUTPUT” box 200. This output is a goodapproximation to both of the primary signals s″_(λa)(t) and s″_(λc)(t)or the secondary signals n″_(λa)(t) and n″_(λc)(t) for the set ofsamples s_(λa)(t) and s_(λc)(t), input to the program. After many setsof samples are processed by the joint process estimator, a compilationof the outputs provides output waves which are good approximations tothe plethysmographic wave or motion artifact at each wavelength, λa andλc.

[0249] Another copy of a computer program subroutine, written in the Cprogramming language, which calculates either a primary reference s′(t)or a secondary reference n′(t) using the constant saturation method and,using a joint process estimator 60, estimates a good approximation toeither the primary signal portions or secondary signal portions of twomeasured signals, each having a primary portion which is correlated tothe primary reference signal s′(t) and a secondary portion which iscorrelated to the secondary reference signal n′(t) and each having beenused to calculate the reference signals s′(t) and n′(t), is appended inAppendix B. This subroutine is another way to implement the stepsillustrated in the flowchart of FIG. 9 for a monitor particularlyadapted for pulse oximetry. The two signals are measured at twodifferent wavelengths λa and λb, where λa is typically in the visibleregion and λb is typically in the infrared region. For example, in oneembodiment of the present invention, tailored specifically to performpulse oximetry using the constant saturation method, λa=660 nm andλb=940 nm.

[0250] The correspondence of the program variables to the variablesdefined in the discussion of the joint process estimator is as follows:

Δ_(m)(t)=nc[m].Delta

Γ_(f,m)(t)=nc[m].fref

Γ_(b,m)(t)=nc[m].bref

f _(m)(t)=nc[m].ferr

b _(m)(t)=nc[m].berr

J _(m)(t)=nc[m].Fswsqr

β_(m)(t)=nc[m].Bswsqr

γ_(m)(t)=nc[m].Gamma

ρ_(m,λa)(t)=nc[m].Roh _(—) a

ρ_(m,λc)(t)=nc[m].Roh _(—) b

e _(m,λa)(t)=nc[m].err _(—) a

e _(m,λc)(t)=nc[m].err _(—) b

κ_(m,λa)(t)=nc[m].K _(—) a

κ_(m,λc)(t)=nc[m].K _(—) b

[0251] First and second portions of the subroutine are the same as thefirst and second portions of the above described subroutine tailored forthe ratiometric method of determining either the primary reference s′(t)or the noise reference n′(t). The calculation of saturation is performedin a separate module. Various methods for calculation of the oxygensaturation are known to those skilled in the art. One such calculationis described in the articles by G. A. Mook, et al, and Michael R. Neumancited above. Once the concentration of oxygenated hemoglobin anddeoxygenated hemoglobin are determined, the value of the saturation isdetermined similarly to equations (85) through (92) wherein measurementsat times t₁ and t₂ are made at different, yet proximate times over whichthe saturation is relatively constant. For pulse oximetry, the averagesaturation at time t=(t₁+t₂)/2 is then determined by: $\begin{matrix}{{{Saturation}_{Art}(t)} = {{c_{Hb02}^{A}(t)}/\left\lbrack {{c_{Hb02}^{A}(t)} + {c_{Hb}^{A}(t)}} \right\rbrack}} & \left( {107a} \right) \\{\quad {= \quad \frac{ɛ_{{Hb},{\lambda \quad a}} - {ɛ_{{Hb},{\lambda \quad b}}\left( {\Delta \quad {s_{\lambda \quad a}/\Delta}\quad s_{\lambda \quad b}} \right)}}{ɛ_{{Hb},{\lambda \quad a}} - ɛ_{{HbO2},{\lambda \quad a}} - {\left( {ɛ_{{Hb},{\lambda \quad b}} - ɛ_{{HbO2},{\lambda \quad b}}} \right)\left( {\Delta \quad {s_{\lambda \quad a}/\Delta}\quad s_{\lambda \quad b}} \right)}}}} & \left( {107b} \right) \\{{{Saturation}_{Ven}(t)} = {{c_{HBO2}^{V}(t)}/\left\lbrack {{c_{HB02}^{V}(t)} + {c_{HB}^{V}(t)}} \right\rbrack}} & \left( {108a} \right) \\{\quad {= \quad \frac{ɛ_{{Hb},{\lambda \quad a}} - {ɛ_{{Hb},{\lambda \quad b}}\left( {\Delta \quad {n_{\lambda \quad a}/\Delta}\quad n_{\lambda \quad b}} \right)}}{ɛ_{{Hb},{\lambda \quad a}} - ɛ_{{HbO2},{\lambda \quad a}} - {\left( {ɛ_{{Hb},{\lambda \quad b}} - ɛ_{{HbO2},{\lambda \quad b}}} \right)\left( {\Delta \quad {n_{\lambda \quad a}/\Delta}\quad n_{\lambda \quad b}} \right)}}}} & \left( {108b} \right)\end{matrix}$

[0252] A third portion of the subroutine calculates either the primaryreference or secondary reference, as in the “CALCULATE PRIMARY ORSECONDARY REFERENCE (s′(t) or n′(t)) FOR TWO MEASURED SIGNAL SAMPLES”box 140 for the signals s_(λa)(t) and s_(λb)(t) using theproportionality constants ω_(a)(t) and ω_(v)(t) determined by theconstant saturation method as in equation (3). The saturation iscalculated in a separate subroutine and a value of ω_(a)(t) or ω_(v)(t)is imported to the present subroutine for estimating either the primaryportions s_(λa)(t) and s_(λb)(t) or the secondary portions n_(λa)(t) andn_(λb)(t) of the composite measured signals s_(λa)(t) and s_(λb)(t).

[0253] Fourth, fifth, and sixth portions of the subroutine are similarto the fourth, fifth, and sixth portions of the above described programtailored for the ratiometric method. However, the signals being used toestimate the primary signal portions s_(λa)(t) and s_(λb)(t) or thesecondary signal portions n_(λa)(t) and n_(λb)(t) in the presentsubroutine tailored for the constant saturation method, are s_(λa)(t)and s_(λb)(t), the same signals that were used to calculate thereference signal s′(t) or n′(t).

[0254] A seventh portion of the program, still within the loop begun inthe fifth portion of the program, calculates the regression coefficientregister 96 and 98 values κ_(m,λa)(t) and κ_(m,λb)(t) (nc[m].K_a andnc[m].K_b in the program) in both regression filters, as in the “ORDERUPDATE m^(th) STAGE OF REGRESSION FILTER(S)” box 180 and equations (68)through (80). Intermediate error signals and variables e_(m,λa)(t),e_(m,λb)(t), ρ_(m,λa)(t), and ρ_(m,λb)(t) (nc[m].err_a and nc[m].err_b,nc[m].roh_a, and nc[m].roh_b in the subroutine) are also calculated asin equations (70), (75), (68), and (71), respectively.

[0255] The loop iterates until the test for convergence is passed, thetest being the same as described above for the subroutine tailored forthe ratiometric method. The output of the present subroutine is a goodapproximation to the primary signals s″_(λa)(t) and s″_(λb)(t) or thesecondary signals n″_(λa)(t) and n″_(λb)(t) for the set of sampless_(λa)(t) and s_(λb)(t) input to the program. After approximations tothe primary signal portions or the secondary signals portions of manysets of measured signal samples are estimated by the joint processestimator, a compilation of the outputs provides waves which are goodapproximations to the plethysmographic wave or motion artifact at eachwavelength, λa and λb. The estimating process of the iterative loop isthe same in either subroutine, only the sample values s_(λa)(t) ands_(λc)(t) or s_(λa)(t) and s_(λb)(t) input to the subroutine for use inestimation of the primary signal portions s_(λa)(t) and s_(λc)(t) ors_(λa)(t) and s_(λb)(t) or of the secondary signal portions n_(λa)(t)and n_(λc)(t) or n_(λa)(t) and n_(λb)(t) and how the primary andsecondary reference signals s′(t) and n′(t) are calculated are differentfor the ratiometric method and the constant saturation methods.

[0256] Independent of the method used, ratiometric or constantsaturation, the approximations to either the primary signal values orthe secondary signal values are input to a separate subroutine in whichthe saturation of oxygen in the arterial and venous blood is calculated.If the constant saturation method is used, the saturation calculationsubroutine also determines values for the proportionality constantsω_(a)(t) and ω_(v)(t) as defined in equation (3) and discussed above.The concentration of oxygenated arterial and venous blood can be foundfrom the approximations to the primary or secondary signal values sincethey are made up of terms comprising x(t), the thickness of arterial andvenous blood in the finger; absorption coefficients of oxygenated andde-oxygenated hemoglobin, at each measured wavelength; and c_(Hb02)(t)and c_(Hb)(t), the concentrations of oxygenated and de-oxygenatedhemoglobin, respectively. The saturation is a ratio of the concentrationof one constituent, A₅, with respect to the total concentration ofconstituents in the volume containing A₅ and A₆ or the ratio of theconcentration of one constituent A₃, with respect to the totalconcentration of constituents in the volume containing A₃ and A₄. Thus,the thickness, x(t), is divided out of the saturation calculation andneed not be predetermined. Additionally, the absorption coefficients areconstant at each wavelength. The saturation of oxygenated arterial andvenous blood is then determined as in equations (107) and (108).

[0257] While one embodiment of a physiological monitor incorporating aprocessor of the present invention for determining a reference signalfor use in a correlation canceler, such as an adaptive noise canceler,to remove or derive primary and secondary components from aphysiological measurement has been described in the form of a pulseoximeter, it will be obvious to one skilled in the art that other typesof physiological monitors may also employ the above describedtechniques.

[0258] Furthermore, the signal processing techniques described in thepresent invention may be used to compute the arterial and venous bloodoxygen saturations of a physiological system on a continuous or nearlycontinuous time basis. These calculations may be performed, regardlessof whether or not the physiological system undergoes voluntary motion.The arterial pulsation induced primary plethysmographic signalss_(λa)(t) and s_(λb)(t) may be used to compute arterial blood oxygensaturation. The primary signals s_(λa)(t) and s_(λb)(t) can always beintroduced into the measured signals s_(λa)(t) and s_(λb)(t) if at leasttwo requirements are met. The two requirements include the selection oftwo or more flesh penetrating. and blood absorbing wavelengths which areoptically modulated by the arterial pulsation and an instrument designwhich passes all or portions of all electromagnetic signals which arerelated to the pulsation. Similarly, the secondary signals n_(λa)(t) andn_(λb)(t) related to venous blood flow may be used to compute itscorresponding oxygen saturation. The secondary signal componentsn_(λa)(t) and n_(λb)(t) can be guaranteed to be contained in themeasured signals s_(λa)(t) and s_(λb)(t) if the two or more fleshpenetrating and blood absorbing wavelengths are processed to pass all orportions of all electromagnetic signals relating to venous blood flow.This may include but is not limited to all or portions of all signalswhich are related to the involuntary action of breathing. Similarly, itmust be understood that there are many different types of physicalsystems which may be configured to yield two or more measurement signalseach possessing a primary and secondary signal portion. In a great manyof such physical systems it will be possible to derive one or morereference signals. The reference signals may be used in conjunction witha correlation canceler, such as an adaptive noise canceler, to deriveeither the primary and/or secondary signal components of the two or moremeasurement signals on a continuous or intermittent time basis.

[0259] Another embodiment of a physiological monitor incorporating aprocessor of the present invention for determining a reference signalfor use in a correlation canceler, such as an adaptive. noise canceler,to remove or derive primary and secondary components from a,physiological measurement may be described in the form of a instrumentwhich measures blood pressure. There are several ways of obtaining bloodpressure measurements, such as tonometry, and pulse wave velocity. Bothof these methods are substantially related to plethysmography.

[0260] Tonometry is a measurement method in which a direct reading ofthe arterial pressure pulse is made non-invasively. These measurementsare invariably made through the use of a piezoelectric force transducer,the surface of which is gently pressed against a near-surface arterysupported by underlying bone. If the transducer is sufficiently pressedagainst the artery that its surface is in complete contact with thetissue; then, knowing its surface area, its output can be directly readas pressure. This “flattening” of the arterial wall leads to the name ofthis method, applanation tonometry. The pulse wave velocity techniquerelies on the concept that the speed with which the pressure pulse,generated at the heart, travels “down” the arterial system is dependenton pressure. In each of these cases plethysmographic waveforms are usedto determine the blood pressure of a patient.

[0261] Furthermore, it will be understood that transformations ofmeasured signals other than logarithmic conversion and determination ofa proportionality factor which allows removal or derivation of theprimary or secondary signal portions for determination of a referencesignal are possible. Additionally, although the proportionality factor(o has been described herein as a ratio of a portion of a first signalto a portion of a second signal, a similar proportionality constantdetermined as a ratio of a portion of a second signal to a portion of a,first signal could equally well be utilized in the processor of thepresent invention. In the latter case, a secondary reference signalwould generally resemble n′(t)=n_(λb)(t)−ωn_(λa)(t).

[0262] Furthermore, it will be understood that correlation cancellationtechniques other than joint process estimation may be used together withthe reference signals of the present invention. These may include butare not limited to least mean square algorithms, wavelet transforms,spectral estimation techniques, neural networks, Weiner filters, Kalmanfilters, QR-decomposition based algorithms among others. Theimplementation that we feel is the best, as of this filing, is thenormalized least square lattice algorithm an implementation of which islisted in Appendix C.

[0263] It will also be obvious to one skilled in the art that for mostphysiological measurements, two wavelengths may be determined which willenable a signal to be measured which is indicative of a quantity of acomponent about which information is desired. Information about aconstituent of any energy absorbing physiological material may bedetermined by a physiological monitor incorporating a signal processorof the present invention and an correlation canceler by determiningwavelengths which are absorbed primarily by the constituent of interest.For most physiological measurements, this is a simple determination.

[0264] Moreover, one skilled in the art will realize that any portion ofa patient or a material derived from a patient may be used to takemeasurements for a physiological monitor incorporating a processor ofthe present invention and a correlation canceler. Such areas include adigit such as a finger, but are not limited to a finger.

[0265] One skilled in the art will realize that many different types ofphysiological monitors may employ a signal processor of the presentinvention in conjunction with a correlation canceler, such as anadaptive noise canceler. Other types of physiological monitors include,but are in not limited to, electron cardiographs, blood pressuremonitors, blood gas saturation (other than oxygen saturation) monitors,capnographs, heart rate monitors, respiration monitors, or depth ofanesthesia monitors. Additionally, monitors which measure the pressureand quantity of a substance within the body such as a breathalizer, adrug monitor, a cholesterol monitor, a glucose monitor, a carbon dioxidemonitor, a glucose monitor, or a carbon monoxide monitor may also employthe above described techniques for removal of primary or secondarysignal portions.

[0266] Furthermore, one skilled in the art will realize that the abovedescribed techniques of primary or secondary signal removal orderivation from a composite signal including both primary and secondarycomponents can also be performed on electrocardiography (ECG) signalswhich are derived from positions on the body which are close and highlycorrelated to each other. It must be understood that a tripolarLaplacian electrode sensor such as that depicted in FIG. 32 which is amodification of a bipolar Laplacian electrode sensor discussed in thearticle “Body Surface Laplacian ECG Mapping” by Bin He and Richard J.Cohen contained in the journal IEEE Transactions on BiomedicalEngineering, Vol. 39, No. 11, November 1992 could be used as an ECGsensor. This article is hereby incorporated as reference. It must alsobe understood that there are a myraid of possible ECG sensor geometry'sthat may be used to satisfy the requirements of the present invention.

[0267] Furthermore, one skilled in the art will realize that the abovedescribed techniques of primary or secondary signal removal orderivation from a composite signal including both primary and secondarycomponents can also be performed on signals made up of reflected energy,rather than transmitted energy. One skilled in the art will also realizethat a primary or secondary portion of a measured signal ofany type ofenergy, including but not limited to sound energy, X-ray energy, gammaray energy, or light energy can be estimated by the techniques describedabove. Thus, one skilled in the art will realize that the processor ofthe present invention and a correlation canceler can be applied in suchmonitors as those using ultrasound where a signal is transmitted througha portion of the body and reflected back from within the body backthrough this portion of the body. Additionally, monitors such as echocardiographs may also utilize the techniques of the present inventionsince they too rely on transmission and reflection.

[0268] While the present invention has been described in terms of aphysiological monitor, one skilled in the art will realize that thesignal processing techniques of the present invention can be applied inmany areas, including but not limited to the processing of aphysiological signal. The present invention may be applied in any.situation where a signal processor comprising a detector receives afirst signal which includes a first primary signal portion and a firstsecondary signal portion and a second signal whicb includes a secondprimary signal portion and a second secondary signal portion. The firstand second signals propagate through a common medium and the first andsecond primary signal portions are correlated with one another.Additionally, at least a portion of the first and second secondarysignal portions are correlated with one another due to a perturbation ofthe medium while the first and second signals are propagating throughthe medium. The processor receives the first and second signals and maycombine the first and second signals to generate a secondary referencein which is uncorrelated with the primary signal portions of themeasured signals or a primary reference which is unicorrelated with thesecondary signal portions of the measured signals. Thus, the signalprocessor of the present invention is readily applicable to numeroussignal processing areas.

1-38. (Cancelled).
 39. An apparatus for computing arterial and venoussignals in living tissue, said apparatus comprising: a detectorconfigured to receive a first signal which travels along a firstpropagation path and a second signal which travels along a secondpropagation path, at least a portion of said first and secondpropagation paths being located in a propagation medium, wherein saidfirst signal has an arterial signal portion that is indicative ofarterial blood and another signal portion that is indicative of venousblood, and said second signal has an arterial signal portion that isindicative of arterial blood and another signal portion that isindicative of venous blood; and a signal processor responsive torepresentations of the first signal and the second signal to generate asignal having a significant component which is a function of saidarterial portions of said first and said second signal.
 40. Theapparatus recited in claim 39, wherein the other signal portion of eachof said first and second signals includes an indication of humanrespiration.
 41. A method determining a value of blood oxygen saturationof pulsing blood, the method comprising: receiving three or moreintensity signals from at least one light-sensitive detector whichdetects light attenuated by body tissue carrying pulsing blood, whereinthe three or more intensity signals correspond to detection of at leastthree wavelengths of the light; and determining a value of oxygensaturation using the three or more intensity signals, wherein the threeor more intensity signals include motion induced noise.
 42. The methodof claim 41, wherein the step of determining further comprisesprocessing at least two of the three or more intensity signals with anadaptive algorithm.
 43. The method of claim 42, wherein the adaptivealgorithm comprises a least squares algorithm.
 44. The method of claim43, wherein the least squares algorithm comprises a least squareslattice.
 45. The method of claim 41, wherein the step of determiningfurther comprises processing at least two of the three or more intensitysignals with a least squares algorithm.
 46. The method of claim 41,wherein one of the three or more intensity signals corresponds to one ofthe at least three wavelengths and is used primarily to reduce an effectof the motion induced noise on the value of oxygen saturation.
 47. Themethod of claim 46, wherein two of the three or more intensity signalscorrespond to two of the at least three wavelengths and are usedprimarily to determine the value of oxygen saturation.
 48. The method ofclaim 41, wherein absorption coefficients associated with two of the atleast three wavelengths are related to one another.
 49. The method ofclaim 48, wherein absorption coefficients associated with two of the atleast three wavelengths are proportional to one another.
 50. The methodof claim 48, wherein absorption coefficients associated with two of theat least three wavelengths are linearly proportional to one another. 51.The method of claim 41, wherein absorption coefficients associated withthe at least three wavelengths are proportional to one another.
 52. Themethod of claim 41, wherein each of the three or more intensity signalsincludes a signal portion and a noise portion, and wherein a non-zeroreference signal corresponds to the noise portion of one of the three ormore intensity signals.
 53. The method of claim 41, wherein each of thethree or more intensity signals includes a signal portion and a noiseportion, and wherein a non-zero reference signal corresponds to thesignal portion of one of the three or more intensity signals.
 54. Themethod of claim 41, wherein the pulsing blood comprises arterial blood.55. The method of claim 41, wherein the pulsing blood comprises venousblood.
 56. A method of reducing an effect of motion induced noise on aplurality of intensity signals during the determination of a parameterof pulsing blood, the method comprising: receiving a plurality ofintensity signals from at least one light−sensitive detector whichdetects light of a plurality of wavelengths attenuated by body tissuecarrying pulsing blood; and processing one of the plurality of intensitysignals using data from an additional intensity signal other than theplurality of intensity signals, the additional intensity signalcorresponding to an additional wavelength of light, wherein theprocessing determines a value of blood oxygen saturation of the pulsingblood during motion induced noise, wherein the data from the extrawavelength is used to reduce an effect of the motion induced noise. 57.The method of claim 56, wherein the step of processing further comprisesprocessing the plurality of intensity signals with an adaptivealgorithm.
 58. The method of claim 57, wherein the adaptive algorithmcomprises a least squares algorithm.
 59. The method of claim 58, whereinthe least squares algorithm comprises a least squares lattice.
 60. Themethod of claim 56, wherein absorption coefficients associated with theplurality of intensity signals are related to one another.
 61. Themethod of claim 60, wherein absorption coefficients associated with theplurality of intensity signals are proportional to one another.
 62. Themethod of claim 60, wherein absorption coefficients associated with theplurality of intensity signals are linearly proportional to one another.63. The method of claim 56, wherein absorption coefficients associatedwith the plurality of intensity signals and the additional intensitysignal are proportional to one another.
 64. The method of claim 56,wherein the plurality of intensity signals and the additional intensitysignal each includes a signal portion and a noise portion, and wherein anon-zero reference signal corresponds to the noise portion of one of theintensity signals.
 65. The method of claim 56, wherein the plurality ofintensity signals and the.additional intensity signal each includes asignal portion and a noise portion, and wherein a non-zero referencesignal corresponds to the signal portion of one of the intensitysignals.
 66. The method of claim 56, wherein the pulsing blood comprisesarterial blood.
 67. The method of claim 56, wherein the pulsing bloodcomprises venous blood.
 68. A physiological monitor for determining aphysiological parameter of a patient, the physiological monitorcomprising: an input which receives three or more intensity signals fromat least one light-sensitive detector which detects light attenuated bybody tissue carrying pulsing blood, wherein the three or more intensitysignals correspond to detection of at least three wavelengths of thelight; and a processor which determines a value of oxygen saturationusing the three or more intensity signals, wherein the three or moreintensity signals include motion induced noise.
 69. The physiologicalmonitor of claim 68, wherein the processor processes at least two of thethree or more intensity signals with an adaptive algorithm.
 70. Thephysiological monitor of claim 69, wherein the adaptive algorithmcomprises a least squares algorithm.
 71. The physiological monitor ofclaim 70, wherein the least squares algorithm comprises a least squareslattice.
 72. The physiological monitor of claim 68, the processorprocesses at least two of the three or more intensity signals with aleast squares algorithm.
 73. The physiological monitor of claim 68,wherein one of the three or more intensity signals corresponds to one ofthe at least three wavelengths and wherein the processor uses the oneintensity signal primarily to reduce an effect of the motion inducednoise on the value of oxygen saturation.
 74. The physiological monitorof claim 73, wherein two of the three or more intensity signalscorresponds to two of the at least three wavelengths and wherein theprocessor uses the one intensity signal primarily to determine the valueof oxygen saturation.
 75. The physiological monitor of claim 68, whereinabsorption coefficients associated with two of the at least threewavelengths are related to one another.
 76. The physiological monitor ofclaim 75, wherein absorption coefficients associated with two of the atleast three wavelengths are proportional to one another.
 77. Thephysiological monitor of claim 75, wherein absorption coefficientsassociated with two of the at least three wavelengths are linearlyproportional to one another.
 78. The physiological monitor of claim 68,wherein absorption coefficients associated with the at least threewavelengths are proportional to one another.
 79. The physiologicalmonitor of claim 68, wherein each of the three or more intensity signalsincludes a signal portion and a noise portion, and wherein a non-zeroreference signal corresponds to the noise portion of one of the three ormore intensity signals.
 80. The physiological monitor of claim 68,wherein each of the three or more intensity signals includes a signalportion and a noise portion, and wherein a non-zero reference signalcorresponds to the signal portion of one of the three or more intensitysignals.
 81. The physiological monitor of claim 68, wherein the pulsingblood comprises arterial blood.
 82. The physiological monitor of claim68, wherein the pulsing blood comprises venous blood.
 83. A method ofdetermining blood oxygen saturation, the method comprising: receiving aplurality of intensity signals representative of light of at least threewavelengths attenuated by body tissue carrying pulsing blood; processingratiometric data related to the plurality of intensity signals to reducean effect of motion induced noise in at least one of the plurality ofintensity signals; determining a value of oxygen saturation from the atleast one of the plurality of intensity signals.
 84. The method of claim83, wherein at least two of the at least three wavelengths correspondsto substantially red light.
 85. The method of claim 83, wherein at leastone of the at least three wavelengths corresponds to substantiallyinfrared light.